[001] The present application claims priority to U.S. Provisional Patent Application No. 63/132,287, filed December 30, 2020, and U.S. Provisional Patent Application No. 63/132,301, filed December 30, 2020. The foregoing applications are incorporated herein by reference in their entirety.

BACKGROUND

I. Technical Field

[002] The present disclosure relates generally to technology for scanning a surrounding environment, and, for example, to systems and methods that use LIDAR technology to detect objects in the surrounding environment.

II. Background Information

[003] With the advent of driver assist systems and autonomous vehicles, automobiles need to be equipped with systems capable of reliably sensing and interpreting their surroundings, including identifying obstacles, hazards, objects, and other physical parameters that might impact navigation of the vehicle. To this end, a number of differing technologies have been suggested including radar, LIDAR, camera-based systems, operating alone or in a redundant manner.

[004] One consideration with driver assistance systems and autonomous vehicles is an ability of the system to determine surroundings across different conditions including, rain, fog, darkness, bright light, and snow. A light detection and ranging system, (LIDAR a/k/a LADAR) is an example of technology that can work well in differing conditions, by measuring distances to objects by illuminating objects with light and measuring the reflected pulses with a sensor. A laser is one example of a light source that can be used in a LIDAR system. As with any sensing system, in order for a LIDAR-based sensing system to be fully adopted by the automotive industry, the system should provide reliable data enabling detection of far-away objects.

[005] The systems and methods of the present disclosure are directed towards improving performance of LIDAR systems.

SUMMARY

[006] Embodiments consistent with the present disclosure provide devices and methods for automatically capturing and processing images from an environment of a user, and systems and methods for processing information related to images captured from the environment of the user. [007] In an embodiment, a LIDAR system for a host vehicle includes a laser emission unit configured to generate at least one laser beam; a scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system; and at least one processor programmed to: define a road surface plane indicative of a portion of a road surface in an environment of the host vehicle; determine at least one indicator of a current pitch of the LIDAR system relative to the road surface plane; compare the current pitch for the LIDAR system to a target pitch for the LIDAR system relative to the road surface plane; and adjust at least one scan range limit associated with the scanning unit to at least partially compensate for a difference between the current pitch of the LIDAR system and the target pitch for the LIDAR system.

[008] In an embodiment, a LIDAR system for a host vehicle includes a laser emission unit configured to generate at least one laser beam; a scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system; and at least one processor programmed to: determine at least one indicator of a current pitch of the LIDAR system; compare the current pitch for the LIDAR system to a target pitch for the LIDAR system; and adjust at least one scan range limit associated with the scanning unit to at least partially compensate for a difference between the current pitch of the LIDAR system and the target pitch for the LIDAR system, wherein the adjustment is initiated after a predetermined time delay.

[009] In an embodiment, LIDAR system for a host vehicle includes a laser emission unit configured to generate at least one laser beam; a biaxial scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system; and at least one processor programmed to: detect a current roll orientation of LIDAR system; determine a difference between the current roll orientation and a target roll orientation for LIDAR system; and adjust a vertical tilt angle of the biaxial scanning unit while adjusting a horizontal scan angle of the biaxial scanning unit to at least partially compensate for the difference between the current roll orientation and a target roll orientation for LIDAR system.

[010] In an embodiment, a LIDAR system for a host vehicle includes a laser emission unit configured to generate at least one laser beam; a scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system; and at least one processor programmed to: determine at least one indicator of a current yaw orientation of the LIDAR system based on analysis of point cloud representations of at least one stationary object in an environment of the host vehicle and based on detected ego motion of the host vehicle; determine a difference between the current yaw orientation and a target yaw orientation for the LIDAR system; and adjust at least one scan range limit associated with the scanning unit to at least partially compensate for a difference between the current yaw orientation of the LIDAR system and the target yaw orientation for the LIDAR system.

[Oil] Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processor and perform any of the methods described herein.

[012] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[013] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings:

[014] Fig. 1A is a diagram illustrating an exemplary LIDAR system consistent with disclosed embodiments.

[015] Fig. IB is an image showing an exemplary output of single scanning cycle of a LIDAR system mounted on a vehicle consistent with disclosed embodiments.

[016] Fig. 1C is another image showing a representation of a point cloud model determined from output of a LIDAR system consistent with disclosed embodiments.

[017] Figs. 2A, 2B, 2C, 2D, 2E, 2F, and 2G are diagrams illustrating different configurations of projecting units in accordance with some embodiments of the present disclosure.

[018] Figs. 3A, 3B, 3C, and 3D are diagrams illustrating different configurations of scanning units in accordance with some embodiments of the present disclosure.

[019] Figs. 4A, 4B, 4C, 4D, and 4E are diagrams illustrating different configurations of sensing units in accordance with some embodiments of the present disclosure.

[020] Fig. 5A includes four example diagrams illustrating emission patterns in a single frame-time for a single portion of the field of view.

[021] Fig. 5B includes three example diagrams illustrating emission scheme in a single frame-time for the whole field of view.

[022] Fig. 5C is a diagram illustrating the actual light emission projected towards and reflections received during a single frame-time for the whole field of view.

[023] Figs. 6A, 6B, and 6C are diagrams illustrating a first example implementation consistent with some embodiments of the present disclosure.

[024] Fig. 6D is a diagram illustrating a second example implementation consistent with some embodiments of the present disclosure.

[025] Fig. 7 is a schematic diagram illustrating a host vehicle, consistent with the disclosed embodiments of the present disclosure. [026] Fig. 8A is schematic diagram illustrating a LIDAR system with an aligned pitch, consistent with the disclosed embodiments of the present disclosure.

[027] Fig. 8B is schematic diagram illustrating a LIDAR system with a misaligned pitch, consistent with the disclosed embodiments of the present disclosure.

[028] Fig. 9A is schematic diagram illustrating a field of view of a LIDAR system with an aligned pitch, according to some embodiments of the present disclosure.

[029] Fig. 9B is schematic diagram illustrating a field of view of a LIDAR system with a misaligned pitch, according to some embodiments of the present disclosure.

[030] Fig. 10A is schematic diagram illustrating a field of view of a LIDAR system with an aligned yaw, according to some embodiments of the present disclosure.

[031] Fig. 10B is schematic diagram illustrating a field of view of a LIDAR system with a misaligned yaw, according to some embodiments of the present disclosure.

[032] Fig. 11 A is schematic diagram illustrating a field of view of a LIDAR system with an aligned roll, according to some embodiments of the present disclosure.

[033] Fig. 11B is schematic diagram illustrating a field of view of a LIDAR system with a misaligned roll, according to some embodiments of the present disclosure.

[034] Fig. 12 is a flow chart of a method for detecting misalignment of a LIDAR system and adjusting the LIDAR’s field of view to compensate for the misalignment, according to some embodiments of the present disclosure.

[035] Fig. 13 is a flow chart of a method for compensating for a LIDAR system’s pitch misalignment without t enlarging the LIDAR system’s field of view, according to some exemplary embodiments of the present disclosure.

[036] Fig. 14 is a schematic diagram illustrating a method for determining an indicator of a pitch of a LIDAR system relative to a road surface plane, according to some exemplary embodiments of the present disclosure.

[037] Fig. 15 A is a flow chart of a method for compensating for a LIDAR system’s pitch misalignment, without enlarging the LIDAR system’ s field of view, according to some exemplary embodiments of the present disclosure.

[038] Fig. 15B is a flow chart of a method for compensating for a LIDAR system’s pitch misalignment, without enlarging the LIDAR system’ s field of view, according to some exemplary embodiments of the present disclosure.

[039] Fig. 15C is a flow chart of a method for compensating for a LIDAR system’s pitch misalignment, without t enlarging the LIDAR system’ s field of view, according to some exemplary embodiments of the present disclosure. [040] Fig. 15D is a flow chart of a method for compensating for a LIDAR system’s pitch misalignment, without enlarging the LIDAR system’ s field of view, according to some exemplary embodiments of the present disclosure.

[041] Fig. 16 is a flow chart of a method for compensating for a LIDAR system’s roll misalignment, without enlarging the LIDAR system’s field of view, according to some exemplary embodiments of the present disclosure.

[042] Fig. 17 is a flow chart of a method for adjusting a LIDAR system’s yaw alignment, according to some exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION

[043] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions or modifications may be made to the components illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims.

Terms Definitions

[044] Disclosed embodiments may involve an optical system. As used herein, the term “optical system” broadly includes any system that is used for the generation, detection and/or manipulation of light. By way of example only, an optical system may include one or more optical components for generating, detecting and/or manipulating light. For example, light sources, lenses, mirrors, prisms, beam splitters, collimators, polarizing optics, optical modulators, optical switches, optical amplifiers, optical detectors, optical sensors, fiber optics, semiconductor optic components, while each not necessarily required, may each be part of an optical system. In addition to the one or more optical components, an optical system may also include other non-optical components such as electrical components, mechanical components, chemical reaction components, and semiconductor components. The non-optical components may cooperate with optical components of the optical system. For example, the optical system may include at least one processor for analyzing detected light.

[045] Consistent with the present disclosure, the optical system may be a LIDAR system. As used herein, the term “LIDAR system” broadly includes any system which can determine values of parameters indicative of a distance between a pair of tangible objects based on reflected light. In one embodiment, the LIDAR system may determine a distance between a pair of tangible objects based on reflections of light emitted by the LIDAR system. As used herein, the term “determine distances” broadly includes generating outputs which are indicative of distances between pairs of tangible objects. The determined distance may represent the physical dimension between a pair of tangible objects. By way of example only, the determined distance may include a line of flight distance between the LIDAR system and another tangible object in a field of view of the LIDAR system. In another embodiment, the LIDAR system may determine the relative velocity between a pair of tangible objects based on reflections of light emitted by the LIDAR system. Examples of outputs indicative of the distance between a pair of tangible objects include: a number of standard length units between the tangible objects (e.g. number of meters, number of inches, number of kilometers, number of millimeters), a number of arbitrary length units (e.g. number of LIDAR system lengths), a ratio between the distance to another length (e.g. a ratio to a length of an object detected in a field of view of the LIDAR system), an amount of time (e.g. given as standard unit, arbitrary units or ratio, for example, the time it takes light to travel between the tangible objects), one or more locations (e.g. specified using an agreed coordinate system, specified in relation to a known location), and more.

[046] The LIDAR system may determine the distance between a pair of tangible objects based on reflected light. In one embodiment, the LIDAR system may process detection results of a sensor which creates temporal information indicative of a period of time between the emission of a light signal and the time of its detection by the sensor. The period of time is occasionally referred to as “time of flight” of the light signal. In one example, the light signal may be a short pulse, whose rise and/or fall time may be detected in reception. Using known information about the speed of light in the relevant medium (usually air), the information regarding the time of flight of the light signal can be processed to provide the distance the light signal traveled between emission and detection. In another embodiment, the LIDAR system may determine the distance based on frequency phase-shift (or multiple frequency phase-shift). Specifically, the LIDAR system may process information indicative of one or more modulation phase shifts (e.g. by solving some simultaneous equations to give a final measure) of the light signal. For example, the emitted optical signal may be modulated with one or more constant frequencies. The at least one phase shift of the modulation between the emitted signal and the detected reflection may be indicative of the distance the light traveled between emission and detection. The modulation may be applied to a continuous wave light signal, to a quasi-continuous wave light signal, or to another type of emitted light signal. It is noted that additional information may be used by the LIDAR system for determining the distance, e.g. location information (e.g. relative positions) between the projection location, the detection location of the signal (especially if distanced from one another), and more.

[047] In some embodiments, the LIDAR system may be used for delecting a plurality of objects in an environment of the LIDAR system. The term “detecting an object in an environment of the LIDAR system” broadly includes generating information which is indicative of an object that reflected light toward a detector associated with the LIDAR system. If more than one object is detected by the LIDAR system, the generated information pertaining to different objects may be interconnected, for example a car is driving on a road, a bird is sitting on the tree, a man touches a bicycle, a van moves towards a building. The dimensions of the environment in which the LIDAR system detects objects may vary with respect to implementation. For example, the LIDAR system may be used for detecting a plurality of objects in an environment of a vehicle on which the LIDAR system is installed, up to a horizontal distance of 100m (or 200m, 300m, etc.), and up to a vertical distance of 10m (or 25m, 50m, etc.). In another example, the LIDAR system may be used for detecting a plurality of objects in an environment of a vehicle or within a predefined horizontal range (e.g., 25° , 50°, 100°, 180°, etc.), and up to a predefined vertical elevation (e.g., ±10°, ±20°, +40°-20°, ±90° or 0°-90°).

[048] As used herein, the term “detecting an object” may broadly refer to determining an existence of the object (e.g., an object may exist in a certain direction with respect to the LIDAR system and/or to another reference location, or an object may exist in a certain spatial volume). Additionally or alternatively, the term “detecting an object” may refer to determining a distance between the object and another location (e.g. a location of the LIDAR system, a location on earth, or a location of another object). Additionally or alternatively, the term “detecting an object” may refer to identifying the object (e.g. classifying a type of object such as car, plant, tree, road; recognizing a specific object (e.g., the Washington Monument); determining a license plate number; determining a composition of an object (e.g., solid, liquid, transparent, semitransparent); determining a kinematic parameter of an object (e.g., whether it is moving, its velocity, its movement direction, expansion of the object). Additionally or alternatively, the term “detecting an object” may refer to generating a point cloud map in which every point of one or more points of the point cloud map correspond to a location in the object or a location on a face thereof. In one embodiment, the data resolution associated with the point cloud map representation of the field of view may be associated with 0.1°x0.1° or 0.3°x0.3° of the field of view.

[049] Consistent with the present disclosure, the term “object” broadly includes a finite composition of matter that may reflect light from at least a portion thereof. For example, an object may be at least partially solid (e.g. cars, trees); at least partially liquid (e.g. puddles on the road, rain); at least partly gaseous (e.g. fumes, clouds); made from a multitude of distinct particles (e.g. sand storm, fog, spray); and may be of one or more scales of magnitude, such as ~1 millimeter (mm), ~5mm, ~10mm, ~50mm, ~100mm, ~500mm, ~1 meter (m), ~5m, ~10m, ~50m, ~100m, and so on. Smaller or larger objects, as well as any size in between those examples, may also be detected. It is noted that for various reasons, the LIDAR system may detect only part of the object. For example, in some cases, light may be reflected from only some sides of the object (e.g., only the side opposing the LIDAR system will be detected); in other cases, light may be projected on only part of the object (e.g. laser beam projected onto a road or a building); in other cases, the object may be partly blocked by another object between the LIDAR system and the detected object; in other cases, the LIDAR’s sensor may only detects light reflected from a portion of the object, e.g., because ambient light or other interferences interfere with detection of some portions of the object.

[050] Consistent with the present disclosure, a LIDAR system may be configured to detect objects by scanning the environment of LIDAR system. The term “scanning the environment of LIDAR system” broadly includes illuminating the field of view or a portion of the field of view of the LIDAR system. In one example, scanning the environment of LIDAR system may be achieved by moving or pivoting a light deflector to deflect light in differing directions toward different parts of the field of view. In another example, scanning the environment of LIDAR system may be achieved by changing a positioning (i.e. location and/or orientation) of a sensor with respect to the field of view. In another example, scanning the environment of LIDAR system may be achieved by changing a positioning (i.e. location and/or orientation) of a light source with respect to the field of view. In yet another example, scanning the environment of LIDAR system may be achieved by changing the positions of at least one light source and of at least one sensor to move rigidly respect to the field of view (i.e. the relative distance and orientation of the at least one sensor and of the at least one light source remains).

[051] As used herein the term “field of view of the LIDAR system” may broadly include an extent of the observable environment of LIDAR system in which objects may be detected. It is noted that the field of view (FOV) of the LIDAR system may be affected by various conditions such as but not limited to: an orientation of the LIDAR system (e.g. is the direction of an optical axis of the LIDAR system); a position of the LIDAR system with respect to the environment (e.g. distance above ground and adjacent topography and obstacles); operational parameters of the LIDAR system (e.g. emission power, computational settings, defined angles of operation), etc. The field of view of LIDAR system may be defined, for example, by a solid angle (e.g. defined using 4>, 0 angles, in which (|) and 0 are angles defined in perpendicular planes, e.g. with respect to symmetry axes of the LIDAR system and/or its FOV). In one example, the field of view may also be defined within a certain range (e.g. up to 200m).

[052] Similarly, the term “instantaneous field of view” may broadly include an extent of the observable environment in which objects may be detected by the LIDAR system at any given moment. For example, for a scanning LIDAR system, the instantaneous field of view is narrower than the entire FOV of the LIDAR system, and it can be moved within the FOV of the LIDAR system in order to enable detection in other parts of the FOV of the LIDAR system. The movement of the instantaneous field of view within the FOV of the LIDAR system may be achieved by moving a light deflector of the LIDAR system (or external to the LIDAR system), so as to deflect beams of light to and/or from the LIDAR system in differing directions. In one embodiment, LIDAR system may be configured to scan scene in the environment in which the LIDAR system is operating. As used herein the term “scene” may broadly include some or all of the objects within the field of view of the LIDAR system, in their relative positions and in their current states, within an operational duration of the LIDAR system. For example, the scene may include ground elements (e.g. earth, roads, grass, sidewalks, road surface marking), sky, man-made objects (e.g. vehicles, buildings, signs), vegetation, people, animals, light projecting elements (e.g. flashlights, sun, other LIDAR systems), and so on.

[053] Disclosed embodiments may involve obtaining information for use in generating reconstructed three-dimensional models. Examples of types of reconstructed three- dimensional models which may be used include point cloud models, and Polygon Mesh (e.g. a triangle mesh). The terms “point cloud” and “point cloud model” are widely known in the art, and should be construed to include a set of data points located spatially in some coordinate system (i.e., having an identifiable location in a space described by a respective coordinate system).The term “point cloud point” refer to a point in space (which may be dimensionless, or a miniature cellular space, e.g. 1 cm ³ ), and whose location may be described by the point cloud model using a set of coordinates (e.g. (X,Y,Z), (r, ,0)). By way of example only, the point cloud model may store additional information for some or all of its points (e.g. color information for points generated from camera images). Likewise, any other type of reconstructed three-dimensional model may store additional information for some or all of its objects. Similarly, the terms “polygon mesh” and “triangle mesh” are widely known in the art, and are to be construed to include, among other things, a set of vertices, edges and faces that define the shape of one or more 3D objects (such as a polyhedral object). The faces may include one or more of the following: triangles (triangle mesh), quadrilaterals, or other simple convex polygons, since this may simplify rendering. The faces may also include more general concave polygons, or polygons with holes. Polygon meshes may be represented using differing techniques, such as: Vertex-vertex meshes, Face-vertex meshes, Winged-edge meshes and Render dynamic meshes. Different portions of the polygon mesh (e.g., vertex, face, edge) are located spatially in some coordinate system (i.e., having an identifiable location in a space described by the respective coordinate system), either directly and/or relative to one another. The generation of the reconstructed three-dimensional model may be implemented using any standard, dedicated and/or novel photogrammetry technique, many of which are known in the art. It is noted that other types of models of the environment may be generated by the LIDAR system.

[054] Consistent with disclosed embodiments, the LIDAR system may include at least one projecting unit with a light source configured to project light. As used herein the term “light source” broadly refers to any device configured to emit light. In one embodiment, the light source may be a laser such as a solid-state laser, laser diode, a high power laser, or an alternative light source such as, a light emitting diode (LED)-based light source. In addition, light source 112 as illustrated throughout the figures, may emit light in differing formats, such as light pulses, continuous wave (CW), quasi-CW, and so on. For example, one type of light source that may be used is a vertical-cavity surface-emitting laser (VCSEL). Another type of light source that may be used is an external cavity diode laser (ECDL). In some examples, the light source may include a laser diode configured to emit light at a wavelength between about 650 nm and 1150 nm. Alternatively, the light source may include a laser diode configured to emit light at a wavelength between about 800 nm and about 1000 nm, between about 850 nm and about 950 nm, or between about 1300 nm and about 1600 nm. Unless indicated otherwise, the term “about” with regards to a numeric value is defined as a variance of up to 5% with respect to the stated value. Additional details on the projecting unit and the at least one light source are described below with reference to Figs. 2A-2C.

[055] Consistent with disclosed embodiments, the LIDAR system may include at least one scanning unit with at least one light deflector configured to deflect light from the light source in order to scan the field of view. The term “light deflector” broadly includes any mechanism or module which is configured to make light deviate from its original path; for example, a mirror, a prism, controllable lens, a mechanical mirror, mechanical scanning polygons, active diffraction (e.g. controllable LCD), Risley prisms, non-mechanical-electro- optical beam steering (such as made by Vscent), polarization grating (such as offered by Boulder Non-Linear Systems), optical phased array (OP A), and more. In one embodiment, a light deflector may include a plurality of optical components, such as at least one reflecting element (e.g. a mirror), at least one refracting element (e.g. a prism, a lens), and so on. In one example, the light deflector may be movable, to cause light deviate to differing degrees (e.g. discrete degrees, or over a continuous span of degrees). The light deflector may optionally be controllable in different ways (e.g. deflect to a degree a, change deflection angle by Aa, move a component of the light deflector by M millimeters, change speed in which the deflection angle changes). In addition, the light deflector may optionally be operable to change an angle of deflection within a single plane (e.g., 0 coordinate). The light deflector may optionally be operable to change an angle of deflection within two non-parallel planes (e.g., 0 and 4> coordinates). Alternatively or in addition, the light deflector may optionally be operable to change an angle of deflection between predetermined settings (e.g. along a predefined scanning route) or otherwise. With respect the use of light deflectors in LIDAR systems, it is noted that a light deflector may be used in the outbound direction (also referred to as transmission direction, or TX) to deflect light from the light source to at least a part of the field of view. However, a light deflector may also be used in the inbound direction (also referred to as reception direction, or RX) to deflect light from at least a part of the field of view to one or more light sensors. Additional details on the scanning unit and the at least one light deflector are described below with reference to Figs. 3A-3C.

[056] Disclosed embodiments may involve pivoting the light deflector in order to scan the field of view. As used herein the term “pivoting” broadly includes rotating of an object (especially a solid object) about one or more axis of rotation, while substantially maintaining a center of rotation fixed. In one embodiment, the pivoting of the light deflector may include rotation of the light deflector about a fixed axis (e.g., a shaft), but this is not necessarily so. For example, in some MEMS mirror implementation, the MEMS mirror may move by actuation of a plurality of benders connected to the mirror, the mirror may experience some spatial translation in addition to rotation. Nevertheless, such mirror may be designed to rotate about a substantially fixed axis, and therefore consistent with the present disclosure it considered to be pivoted. In other embodiments, some types of light deflectors (e.g. non-mechanical-electro-optical beam steering, OP A) do not require any moving components or internal movements in order to change the deflection angles of deflected light. It is noted that any discussion relating to moving or pivoting a light deflector is also mutatis mutandis applicable to controlling the light deflector such that it changes a deflection behavior of the light deflector. For example, controlling the light deflector may cause a change in a deflection angle of beams of light arriving from at least one direction. [057] Disclosed embodiments may involve receiving reflections associated with a portion of the field of view corresponding to a single instantaneous position of the light deflector. As used herein, the term “instantaneous position of the light deflector” (also referred to as “state of the light deflector”) broadly refers to the location or position in space where at least one controlled component of the light deflector is situated at an instantaneous point in time, or over a short span of time. In one embodiment, the instantaneous position of light deflector may be gauged with respect to a frame of reference. The frame of reference may pertain to at least one fixed point in the LIDAR system. Or, for example, the frame of reference may pertain to at least one fixed point in the scene. In some embodiments, the instantaneous position of the light deflector may include some movement of one or more components of the light deflector (e.g. mirror, prism), usually to a limited degree with respect to the maximal degree of change during a scanning of the field of view. For example, a scanning of the entire the field of view of the LIDAR system may include changing deflection of light over a span of 30°, and the instantaneous position of the at least one light deflector may include angular shifts of the light deflector within 0.05°. In other embodiments, the term “instantaneous position of the light deflector” may refer to the positions of the light deflector during acquisition of light which is processed to provide data for a single point of a point cloud (or another type of 3D model) generated by the LIDAR system. In some embodiments, an instantaneous position of the light deflector may correspond with a fixed position or orientation in which the deflector pauses for a short time during illumination of a particular sub-region of the LIDAR field of view. In other cases, an instantaneous position of the light deflector may correspond with a certain position/orientation along a scanned range of positions/orientations of the light deflector that the light deflector passes through as part of a continuous or semi-continuous scan of the LIDAR field of view. In some embodiments, the light deflector may be moved such that during a scanning cycle of the LIDAR FOV the light deflector is located at a plurality of different instantaneous positions. In other words, during the period of time in which a scanning cycle occurs, the deflector may be moved through a series of different instantaneous positions/orientations, and the deflector may reach each different instantaneous position/orientation at a different time during the scanning cycle.

[058] Consistent with disclosed embodiments, the LIDAR system may include at least one sensing unit with at least one sensor configured to detect reflections from objects in the field of view. The term “sensor” broadly includes any device, element, or system capable of measuring properties (e.g., power, frequency, phase, pulse timing, pulse duration) of electromagnetic waves and to generate an output relating to the measured properties. In some embodiments, the at least one sensor may include a plurality of detectors constructed from a plurality of detecting elements. The at least one sensor may include light sensors of one or more types. It is noted that the at least one sensor may include multiple sensors of the same type which may differ in other characteristics (e.g., sensitivity, size). Other types of sensors may also be used. Combinations of several types of sensors can be used for different reasons, such as improving detection over a span of ranges (especially in close range); improving the dynamic range of the sensor; improving the temporal response of the sensor; and improving detection in varying environmental conditions (e.g. atmospheric temperature, rain, etc.).In one embodiment, the at least one sensor includes a SiPM (Silicon photomultipliers) which is a solid-state single-photon-sensitive device built from an array of avalanche photodiode (APD), single photon avalanche diode (SPAD), serving as detection elements on a common silicon substrate. In one example, a typical distance between SPADs may be between about 10pm and about 50pm, wherein each SPAD may have a recovery time of between about 20ns and about 100ns. Similar photomultipliers from other, non-silicon materials may also be used. Although a SiPM device works in digital/switching mode, the SiPM is an analog device because all the microcells may be read in parallel, making it possible to generate signals within a dynamic range from a single photon to hundreds and thousands of photons detected by the different SPADs. It is noted that outputs from different types of sensors (e.g., SPAD, APD, SiPM, PIN diode, Photodetector) may be combined together to a single output which may be processed by a processor of the LIDAR system. Additional details on the sensing unit and the at least one sensor are described below with reference to Figs. 4A-4C.

[059] Consistent with disclosed embodiments, the LIDAR system may include or communicate with at least one processor configured to execute differing functions. The at least one processor may constitute any physical device having an electric circuit that performs a logic operation on input or inputs. For example, the at least one processor may include one or more integrated circuits (IC), including Application-specific integrated circuit (ASIC), microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), or other circuits suitable for executing instructions or performing logic operations. The instructions executed by at least one processor may, for example, be pre- loaded into a memory integrated with or embedded into the controller or may be stored in a separate memory. The memory may comprise a Random Access Memory (RAM), a Read- Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions. In some embodiments, the memory is configured to store information representative data about objects in the environment of the LIDAR system. In some embodiments, the at least one processor may include more than one processor. Each processor may have a similar construction or the processors may be of differing constructions that are electrically connected or disconnected from each other. For example, the processors may be separate circuits or integrated in a single circuit. When more than one processor is used, the processors may be configured to operate independently or collaboratively. The processors may be coupled electrically, magnetically, optically, acoustically, mechanically or by other means that permit them to interact. Additional details on the processing unit and the at least one processor are described below with reference to Figs. 5A-5C.

System Overview

[060] Fig. 1A illustrates a LIDAR system 100 including a projecting unit 102, a scanning unit 104, a sensing unit 106, and a processing unit 108. LIDAR system 100 may be mountable on a vehicle 110. Consistent with embodiments of the present disclosure, projecting unit 102 may include at least one light source 112, scanning unit 104 may include at least one light deflector 114, sensing unit 106 may include at least one sensor 116, and processing unit 108 may include at least one processor 118. In one embodiment, at least one processor 118 may be configured to coordinate operation of the at least one light source 112 with the movement of at least one light deflector 114 in order to scan a field of view 120. During a scanning cycle, each instantaneous position of at least one light deflector 114 may be associated with a particular portion 122 of field of view 120. In addition, LIDAR system 100 may include at least one optional optical window 124 for directing light projected towards field of view 120 and/or receiving light reflected from objects in field of view 120. Optional optical window 124 may serve different purposes, such as collimation of the projected light and focusing of the reflected light. In one embodiment, optional optical window 124 may be an opening, a flat window, a lens, or any other type of optical window.

[061] Consistent with the present disclosure, LIDAR system 100 may be used in autonomous or semi-autonomous road- vehicles (for example, cars, buses, vans, trucks and any other terrestrial vehicle). Autonomous road-vehicles with LIDAR system 100 may scan their environment and drive to a destination vehicle without human input. Similarly, LIDAR system 100 may also be used in autonomous/semi-autonomous aerial-vehicles (for example, UAV, drones, quadcopters, and any other airborne vehicle or device); or in an autonomous or semi- autonomous water vessel (e.g., boat, ship, submarine, or any other watercraft). Autonomous aerial- vehicles and water craft with LIDAR system 100 may scan their environment and navigate to a destination autonomously or using a remote human operator. According to one embodiment, vehicle 110 (either a road-vehicle, aerial- vehicle, or watercraft) may use LIDAR system 100 to aid in detecting and scanning the environment in which vehicle 110 is operating.

[062] It should be noted that LIDAR system 100 or any of its components may be used together with any of the example embodiments and methods disclosed herein. Further, while some aspects of LIDAR system 100 are described relative to an exemplary vehiclebased LIDAR platform, LIDAR system 100, any of its components, or any of the processes described herein may be applicable to LIDAR systems of other platform types.

[063] In some embodiments, LIDAR system 100 may include one or more scanning units 104 to scan the environment around vehicle 110. LIDAR system 100 may be attached or mounted to any part of vehicle 110. Sensing unit 106 may receive reflections from the surroundings of vehicle 110, and transfer reflections signals indicative of light reflected from objects in field of view 120 to processing unit 108. Consistent with the present disclosure, scanning units 104 may be mounted to or incorporated into a bumper, a fender, a side panel, a spoiler, a roof, a headlight assembly, a taillight assembly, a rear-view mirror assembly, a hood, a trunk or any other suitable part of vehicle 110 capable of housing at least a portion of the LIDAR system. In some cases, LIDAR system 100 may capture a complete surround view of the environment of vehicle 110. Thus, LIDAR system 100 may have a 360-degree horizontal field of view. In one example, as shown in Fig. 1A, LIDAR system 100 may include a single scanning unit 104 mounted on a roof vehicle 110. Alternatively, LIDAR system 100 may include multiple scanning units (e.g., two, three, four, or more scanning units 104) each with a field of few such that in the aggregate the horizontal field of view is covered by a 360-degree scan around vehicle 110. One skilled in the art will appreciate that LIDAR system 100 may include any number of scanning units 104 arranged in any manner, each with an 80° to 120° field of view or less, depending on the number of units employed. Moreover, a 360-degree horizontal field of view may be also obtained by mounting a multiple LIDAR systems 100 on vehicle 110, each with a single scanning unit 104. It is nevertheless noted, that the one or more LIDAR systems 100 do not have to provide a complete 360° field of view, and that narrower fields of view may be useful in some situations. For example, vehicle 110 may require a first LIDAR system 100 having an field of view of 75° looking ahead of the vehicle, and possibly a second LIDAR system 100 with a similar FOV looking backward (optionally with a lower detection range). It is also noted that different vertical field of view angles may also be implemented.

[064] Fig. IB is an image showing an exemplary output from a single scanning cycle of LIDAR system 100 mounted on vehicle 110 consistent with disclosed embodiments. In this example, scanning unit 104 is incorporated into a right headlight assembly of vehicle 110. Every gray dot in the image corresponds to a location in the environment around vehicle 110 determined from reflections detected by sensing unit 106. In addition to location, each gray dot may also be associated with different types of information, for example, intensity (e.g., how much light returns back from that location), reflectivity, proximity to other dots, and more. In one embodiment, LIDAR system 100 may generate a plurality of point-cloud data entries from detected reflections of multiple scanning cycles of the field of view to enable, for example, determining a point cloud model of the environment around vehicle 110.

[065] Fig. 1C is an image showing a representation of the point cloud model determined from the output of LIDAR system 100. Consistent with disclosed embodiments, by processing the generated point-cloud data entries of the environment around vehicle 110, a surround- view image may be produced from the point cloud model. In one embodiment, the point cloud model may be provided to a feature extraction module, which processes the point cloud information to identify a plurality of features. Each feature may include data about different aspects of the point cloud and/or of objects in the environment around vehicle 110 (e.g. cars, trees, people, and roads). Features may have the same resolution of the point cloud model (i.e. having the same number of data points, optionally arranged into similar sized 2D arrays), or may have different resolutions. The features may be stored in any kind of data structure (e.g. raster, vector, 2D array, ID array). In addition, virtual features, such as a representation of vehicle 110, border lines, or bounding boxes separating regions or objects in the image (e.g., as depicted in Fig. IB), and icons representing one or more identified objects, may be overlaid on the representation of the point cloud model to form the final surroundview image. For example, a symbol of vehicle 110 may be overlaid at a center of the surround- view image.

The Projecting Unit

[066] Figs. 2A-2G depict various configurations of projecting unit 102 and its role in LIDAR system 100. Specifically, Fig. 2A is a diagram illustrating projecting unit 102 with a single light source; Fig. 2B is a diagram illustrating a plurality of projecting units 102 with a plurality of light sources aimed at a common light deflector 114; Fig. 2C is a diagram illustrating projecting unit 102 with a primary and a secondary light sources 112; Fig. 2D is a diagram illustrating an asymmetrical deflector used in some configurations of projecting unit 102; Fig. 2E is a diagram illustrating a first configuration of a non-scanning LIDAR system; Fig. 2F is a diagram illustrating a second configuration of a non-scanning LIDAR system; and Fig. 2G is a diagram illustrating a LIDAR system that scans in the outbound direction and does not scan in the inbound direction. One skilled in the art will appreciate that the depicted configurations of projecting unit 102 may have numerous variations and modifications. [067] Fig. 2A illustrates an example of a bi-static configuration of LIDAR system 100 in which projecting unit 102 includes a single light source 112. The term “bi-static configuration” broadly refers to LIDAR systems configurations in which the projected light exiting the LIDAR system and the reflected light entering the LIDAR system pass through substantially different optical paths. In some embodiments, a bi-static configuration of LIDAR system 100 may include a separation of the optical paths by using completely different optical components, by using parallel but not fully separated optical components, or by using the same optical components for only part of the of the optical paths (optical components may include, for example, windows, lenses, mirrors, beam splitters, etc.). In the example depicted in Fig. 2A, the bi-static configuration includes a configuration where the outbound light and the inbound light pass through a single optical window 124 but scanning unit 104 includes two light deflectors, a first light deflector 114A for outbound light and a second light deflector 114B for inbound light (the inbound light in LIDAR system includes emitted light reflected from objects in the scene, and may also include ambient light arriving from other sources). In the examples depicted in Figs. 2E and 2G, the bi-static configuration includes a configuration where the outbound light passes through a first optical window 124A, and the inbound light passes through a second optical window 124B. In all the example configurations above, the inbound and outbound optical paths differ from one another.

[068] In this embodiment, all the components of LIDAR system 100 may be contained within a single housing 200, or may be divided among a plurality of housings. As shown, projecting unit 102 is associated with a single light source 112 that includes a laser diode 202 A (or one or more laser diodes coupled together) configured to emit light (projected light 204). In one non-limiting example, the light projected by light source 112 may be at a wavelength between about 800 nm and 950 nm, have an average power between about 50 mW and about 500 mW, have a peak power between about 50 W and about 200 W, and a pulse width of between about 2 ns and about 100 ns. In addition, light source 112 may optionally be associated with optical assembly 202B used for manipulation of the light emitted by laser diode 202A (e.g. for collimation, focusing, etc.). It is noted that other types of light sources 112 may be used, and that the disclosure is not restricted to laser diodes. In addition, light source 112 may emit its light in different formats, such as light pulses, frequency modulated, continuous wave (CW), quasi-CW, or any other form corresponding to the particular light source employed. The projection format and other parameters may be changed by the light source from time to time based on different factors, such as instructions from processing unit 108. The projected light is projected towards an outbound deflector 114A that functions as a steering element for directing the projected light in field of view 120. In this example, scanning unit 104 also include a pivotable return deflector 114B that direct photons (reflected light 206) reflected back from an object 208 within field of view 120 toward sensor 116. The reflected light is detected by sensor 116 and information about the object (e.g., the distance to object 212) is determined by processing unit 108.

[069] In this figure, LIDAR system 100 is connected to a host 210. Consistent with the present disclosure, the term “host” refers to any computing environment that may interface with LIDAR system 100, it may be a vehicle system (e.g., part of vehicle 110), a testing system, a security system, a surveillance system, a traffic control system, an urban modelling system, or any system that monitors its surroundings. Such computing environment may include at least one processor and/or may be connected LIDAR system 100 via the cloud. In some embodiments, host 210 may also include interfaces to external devices such as camera and sensors configured to measure different characteristics of host 210 (e.g., acceleration, steering wheel deflection, reverse drive, etc.). Consistent with the present disclosure, LIDAR system 100 may be fixed to a stationary object associated with host 210 (e.g. a building, a tripod) or to a portable system associated with host 210 (e.g., a portable computer, a movie camera). Consistent with the present disclosure, LIDAR system 100 may be connected to host 210, to provide outputs of LIDAR system 100 (e.g., a 3D model, a reflectivity image) to host 210. Specifically, host 210 may use LIDAR system 100 to aid in detecting and scanning the environment of host 210 or any other environment. In addition, host 210 may integrate, synchronize or otherwise use together the outputs of LIDAR system 100 with outputs of other sensing systems (e.g. cameras, microphones, radar systems). In one example, LIDAR system 100 may be used by a security system. An example of such an embodiment is described below with reference to Fig. 6D.

[070] LIDAR system 100 may also include a bus 212 (or other communication mechanisms) that interconnect subsystems and components for transferring information within LIDAR system 100. Optionally, bus 212 (or another communication mechanism) may be used for interconnecting LIDAR system 100 with host 210. In the example of Fig. 2A, processing unit 108 includes two processors 118 to regulate the operation of projecting unit 102, scanning unit 104, and sensing unit 106 in a coordinated manner based, at least partially, on information received from internal feedback of LIDAR system 100. In other words, processing unit 108 may be configured to dynamically operate LIDAR system 100 in a closed loop. A closed loop system is characterized by having feedback from at least one of the elements and updating one or more parameters based on the received feedback. Moreover, a closed loop system may receive feedback and update its own operation, at least partially, based on that feedback. A dynamic system or element is one that may be updated during operation.

[071] According to some embodiments, scanning the environment around LIDAR system 100 may include illuminating field of view 120 with light pulses. The light pulses may have parameters such as: pulse duration, pulse angular dispersion, wavelength, instantaneous power, photon density at different distances from light source 112, average power, pulse power intensity, pulse width, pulse repetition rate, pulse sequence, pulse duty cycle, wavelength, phase, polarization, and more. Scanning the environment around LIDAR system 100 may also include detecting and characterizing various aspects of the reflected light. Characteristics of the reflected light may include, for example: time-of-flight (i.e., time from emission until detection), instantaneous power (e.g., power signature), average power across entire return pulse, and photon distribution/signal over return pulse period. By comparing characteristics of a light pulse with characteristics of corresponding reflections, a distance and possibly a physical characteristic, such as reflected intensity of object 212 may be estimated. By repeating this process across multiple adjacent portions 122, in a predefined pattern (e.g., raster, Lissajous or other patterns) an entire scan of field of view 120 may be achieved. As discussed below in greater detail, in some situations LIDAR system 100 may direct light to only some of the portions 122 in field of view 120 at every scanning cycle. These portions may be adjacent to each other, but not necessarily so.

[072] In another embodiment, LIDAR system 100 may include network interface 214 for communicating with host 210 (e.g., a vehicle controller). The communication between LIDAR system 100 and host 210 is represented by a dashed arrow. In one embodiment, network interface 214 may include an integrated service digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, network interface 214 may include a local area network (LAN) card to provide a data communication connection to a compatible LAN. In another embodiment, network interface 214 may include an Ethernet port connected to radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of network interface 214 depends on the communications network(s) over which LIDAR system 100 and host 210 are intended to operate. For example, network interface 214 may be used, for example, to provide outputs of LIDAR system 100 to the external system, such as a 3D model, operational parameters of LIDAR system 100, and so on. In other embodiment, the communication unit may be used, for example, to receive instructions from the external system, to receive information regarding the inspected environment, to receive information from another sensor, etc. [073] Fig. 2B illustrates an example of a monostatic configuration of LIDAR system 100 including a plurality projecting units 102. The term “monostatic configuration” broadly refers to LIDAR system configurations in which the projected light exiting from the LIDAR system and the reflected light entering the LIDAR system pass through substantially similar optical paths. In one example, the outbound light beam and the inbound light beam may share at least one optical assembly through which both outbound and inbound light beams pass. In another example, the outbound light may pass through an optical window (not shown) and the inbound light radiation may pass through the same optical window. A monostatic configuration may include a configuration where the scanning unit 104 includes a single light deflector 114 that directs the projected light towards field of view 120 and directs the reflected light towards a sensor 116. As shown, both projected light 204 and reflected light 206 hits an asymmetrical deflector 216. The term “asymmetrical deflector” refers to any optical device having two sides capable of deflecting a beam of light hitting it from one side in a different direction than it deflects a beam of light hitting it from the second side. In one example, the asymmetrical deflector does not deflect projected light 204 and deflects reflected light 206 towards sensor 116. One example of an asymmetrical deflector may include a polarization beam splitter. In another example, asymmetrical 216 may include an optical isolator that allows the passage of light in only one direction. A diagrammatic representation of asymmetrical deflector 216 is illustrated in Fig. 2D. Consistent with the present disclosure, a monostatic configuration of LIDAR system 100 may include an asymmetrical deflector to prevent reflected light from hitting light source 112, and to direct all the reflected light toward sensor 116, thereby increasing detection sensitivity.

[074] In the embodiment of Fig. 2B, LIDAR system 100 includes three projecting units 102 each with a single of light source 112 aimed at a common light deflector 114. In one embodiment, the plurality of light sources 112 (including two or more light sources) may project light with substantially the same wavelength and each light source 112 is generally associated with a differing area of the field of view (denoted in the figure as 120A, 120B, and 120C). This enables scanning of a broader field of view than can be achieved with a light source 112. In another embodiment, the plurality of light sources 102 may project light with differing wavelengths, and all the light sources 112 may be directed to the same portion (or overlapping portions) of field of view 120.

[075] Fig. 2C illustrates an example of LIDAR system 100 in which projecting unit 102 includes a primary light source 112A and a secondary light source 112B. Primary light source 112 A may project light with a longer wavelength than is sensitive to the human eye in order to optimize SNR and detection range. For example, primary light source 112A may project light with a wavelength between about 750 nm and 1100 nm. In contrast, secondary light source 112B may project light with a wavelength visible to the human eye. For example, secondary light source 112B may project light with a wavelength between about 400 nm and 700 nm. In one embodiment, secondary light source 112B may project light along substantially the same optical path the as light projected by primary light source 112A. Both light sources may be time- synchronized and may project light emission together or in interleaved pattern. An interleave pattern means that the light sources are not active at the same time which may mitigate mutual interference. A person who is of skill in the art would readily see that other combinations of wavelength ranges and activation schedules may also be implemented.

[076] Consistent with some embodiments, secondary light source 112B may cause human eyes to blink when it is too close to the LIDAR optical output port. This may ensure an eye safety mechanism not feasible with typical laser sources that utilize the near-infrared light spectrum. In another embodiment, secondary light source 112B may be used for calibration and reliability at a point of service, in a manner somewhat similar to the calibration of headlights with a special reflector/pattem at a certain height from the ground with respect to vehicle 110. An operator at a point of service could examine the calibration of the LIDAR by simple visual inspection of the scanned pattern over a featured target such a test pattern board at a designated distance from LIDAR system 100. In addition, secondary light source 112B may provide means for operational confidence that the LIDAR is working for the end-user. For example, the system may be configured to permit a human to place a hand in front of light deflector 114 to test its operation.

[077] Secondary light source 112B may also have a non- visible element that can double as a backup system in case primary light source 112A fails. This feature may be useful for fail-safe devices with elevated functional safety ratings. Given that secondary light source 112B may be visible and also due to reasons of cost and complexity, secondary light source 112B may be associated with a smaller power compared to primary light source 112A. Therefore, in case of a failure of primary light source 112A, the system functionality will fall back to secondary light source 112B set of functionalities and capabilities. While the capabilities of secondary light source 112B may be inferior to the capabilities of primary light source 112A, LIDAR system 100 system may be designed in such a fashion to enable vehicle 110 to safely arrive its destination.

[078] Fig. 2D illustrates asymmetrical deflector 216 that may be part of LIDAR system 100. In the illustrated example, asymmetrical deflector 216 includes a reflective surface 218 (such as a mirror) and a one-way deflector 220. While not necessarily so, asymmetrical deflector 216 may optionally be a static deflector. Asymmetrical deflector 216 may be used in a monostatic configuration of LIDAR system 100, in order to allow a common optical path for transmission and for reception of light via the at least one deflector 114, e.g. as illustrated in Figs. 2B and 2C. However, typical asymmetrical deflectors such as beam splitters are characterized by energy losses, especially in the reception path, which may be more sensitive to power loses than the transmission path.

[079] As depicted in Fig. 2D, LIDAR system 100 may include asymmetrical deflector 216 positioned in the transmission path, which includes one-way deflector 220 for separating between the transmitted and received light signals. Optionally, one-way deflector 220 may be substantially transparent to the transmission light and substantially reflective to the received light. The transmitted light is generated by projecting unit 102 and may travel through one-way deflector 220 to scanning unit 104 which deflects it towards the optical outlet. The received light arrives through the optical inlet, to the at least one deflecting element 114, which deflects the reflections signal into a separate path away from the light source and towards sensing unit 106. Optionally, asymmetrical deflector 216 may be combined with a polarized light source 112 which is linearly polarized with the same polarization axis as one-way deflector 220. Notably, the cross-section of the outbound light beam is much smaller than that of the reflections signals. Accordingly, LIDAR system 100 may include one or more optical components (e.g. lens, collimator) for focusing or otherwise manipulating the emitted polarized light beam to the dimensions of the asymmetrical deflector 216. In one embodiment, one-way deflector 220 may be a polarizing beam splitter that is virtually transparent to the polarized light beam.

[080] Consistent with some embodiments, LIDAR system 100 may further include optics 222 (e.g., a quarter wave plate retarder) for modifying a polarization of the emitted light. For example, optics 222 may modify a linear polarization of the emitted light beam to circular polarization. Light reflected back to system 100 from the field of view would arrive back through deflector 114 to optics 222, bearing a circular polarization with a reversed handedness with respect to the transmitted light. Optics 222 would then convert the received reversed handedness polarization light to a linear polarization that is not on the same axis as that of the polarized beam splitter 216. As noted above, the received light-patch is larger than the transmitted light-patch, due to optical dispersion of the beam traversing through the distance to the target.

[081] Some of the received light will impinge on one-way deflector 220 that will reflect the light towards sensor 106 with some power loss. However, another part of the received patch of light will fall on a reflective surface 218 which surrounds one-way deflector 220 (e.g., polarizing beam splitter slit). Reflective surface 218 will reflect the light towards sensing unit 106 with substantially zero power loss. One-way deflector 220 would reflect light that is composed of various polarization axes and directions that will eventually arrive at the detector. Optionally, sensing unit 106 may include sensor 116 that is agnostic to the laser polarization, and is primarily sensitive to the amount of impinging photons at a certain wavelength range.

[082] It is noted that the proposed asymmetrical deflector 216 provides far superior performances when compared to a simple mirror with a passage hole in it. In a mirror with a hole, all of the reflected light which reaches the hole is lost to the detector. However, in deflector 216, one-way deflector 220 deflects a significant portion of that light (e.g., about 50%) toward the respective sensor 116. In LIDAR systems, the number photons reaching the LIDAR from remote distances is very limited, and therefore the improvement in photon capture rate is important.

[083] According to some embodiments, a device for beam splitting and steering is described. A polarized beam may be emitted from a light source having a first polarization. The emitted beam may be directed to pass through a polarized beam splitter assembly. The polarized beam splitter assembly includes on a first side a one-directional slit and on an opposing side a mirror. The one-directional slit enables the polarized emitted beam to travel toward a quarter- wave-plate/wave -retarder which changes the emitted signal from a polarized signal to a linear signal (or vice versa) so that subsequently reflected beams cannot travel through the one-directional slit.

[084] Fig. 2E shows an example of a bi-static configuration of LIDAR system 100 without scanning unit 104. In order to illuminate an entire field of view (or substantially the entire field of view) without deflector 114, projecting unit 102 may optionally include an array of light sources (e.g., 112A-112F). In one embodiment, the array of light sources may include a linear array of light sources controlled by processor 118. For example, processor 118 may cause the linear array of light sources to sequentially project collimated laser beams towards first optional optical window 124A. First optional optical window 124A may include a diffuser lens for spreading the projected light and sequentially forming wide horizontal and narrow vertical beams. Optionally, some or all of the at least one light source 112 of system 100 may project light concurrently. For example, processor 118 may cause the array of light sources to simultaneously project light beams from a plurality of non-adjacent light sources 112. In the depicted example, light source 112A, light source 112D, and light source 112F simultaneously project laser beams towards first optional optical window 124A thereby illuminating the field of view with three narrow vertical beams. The light beam from fourth light source 112D may reach an object in the field of view. The light reflected from the object may be captured by second optical window 124B and may be redirected to sensor 116. The configuration depicted in Fig. 2E is considered to be a bi-static configuration because the optical paths of the projected light and the reflected light are substantially different. It is noted that projecting unit 102 may also include a plurality of light sources 112 arranged in nonlinear configurations, such as a two dimensional array, in hexagonal tiling, or in any other way.

[085] Fig. 2F illustrates an example of a monostatic configuration of LIDAR system 100 without scanning unit 104. Similar to the example embodiment represented in Fig. 2E, in order to illuminate an entire field of view without deflector 114, projecting unit 102 may include an array of light sources (e.g., 112A-112F). But, in contrast to Fig. 2E, this configuration of LIDAR system 100 may include a single optical window 124 for both the projected light and for the reflected light. Using asymmetrical deflector 216, the reflected light may be redirected to sensor 116. The configuration depicted in Fig. 2E is considered to be a monostatic configuration because the optical paths of the projected light and the reflected light are substantially similar to one another. The term “substantially similar” in the context of the optical paths of the projected light and the reflected light means that the overlap between the two optical paths may be more than 80%, more than 85%, more than 90%, or more than 95%.

[086] Fig. 2G illustrates an example of a bi-static configuration of LIDAR system 100. The configuration of LIDAR system 100 in this figure is similar to the configuration shown in Fig. 2 A. For example, both configurations include a scanning unit 104 for directing projected light in the outbound direction toward the field of view. But, in contrast to the embodiment of Fig. 2A, in this configuration, scanning unit 104 does not redirect the reflected light in the inbound direction. Instead the reflected light passes through second optical window 124B and enters sensor 116. The configuration depicted in Fig. 2G is considered to be a bi-static configuration because the optical paths of the projected light and the reflected light are substantially different from one another. The term “substantially different” in the context of the optical paths of the projected light and the reflected light means that the overlap between the two optical paths may be less than 10%, less than 5%, less than 1%, or less than 0.25%.

The Scanning Unit

[087] Figs. 3A-3D depict various configurations of scanning unit 104 and its role in LIDAR system 100. Specifically, Fig. 3 A is a diagram illustrating scanning unit 104 with a MEMS mirror (e.g., square shaped), Fig. 3B is a diagram illustrating another scanning unit 104 with a MEMS mirror (e.g., round shaped), Fig. 3C is a diagram illustrating scanning unit 104 with an array of reflectors used for monostatic scanning LIDAR system, and Fig. 3D is a diagram illustrating an example LIDAR system 100 that mechanically scans the environment around LIDAR system 100. One skilled in the art will appreciate that the depicted configurations of scanning unit 104 are exemplary only, and may have numerous variations and modifications within the scope of this disclosure.

[088] Fig. 3A illustrates an example scanning unit 104 with a single axis square MEMS mirror 300. In this example MEMS mirror 300 functions as at least one deflector 114. As shown, scanning unit 104 may include one or more actuators 302 (specifically, 302A and 302B). In one embodiment, actuator 302 may be made of semiconductor (e.g., silicon) and includes a piezoelectric layer (e.g. PZT, Lead zirconate titanate, aluminum nitride), which changes its dimension in response to electric signals applied by an actuation controller, a semi conductive layer, and a base layer. In one embodiment, the physical properties of actuator 302 may determine the mechanical stresses that actuator 302 experiences when electrical current passes through it. When the piezoelectric material is activated it exerts force on actuator 302 and causes it to bend. In one embodiment, the resistivity of one or more actuators 302 may be measured in an active state (Ractive) when mirror 300 is deflected at a certain angular position and compared to the resistivity at a resting state (Rrest). Feedback including Ractive may provide information to determine the actual mirror deflection angle compared to an expected angle, and, if needed, mirror 300 deflection may be corrected. The difference between Rrest and Ractive may be correlated by a mirror drive into an angular deflection value that may serve to close the loop. This embodiment may be used for dynamic tracking of the actual mirror position and may optimize response, amplitude, deflection efficiency, and frequency for both linear mode and resonant mode MEMS mirror schemes. This embodiment is described in greater detail below with reference to Figs. 32-34.

[089] During scanning, current (represented in the figure as the dashed line) may flow from contact 304A to contact 304B (through actuator 302A, spring 306A, mirror 300, spring 306B, and actuator 302B). Isolation gaps in semiconducting frame 308 such as isolation gap 310 may cause actuator 302A and 302B to be two separate islands connected electrically through springs 306 and frame 308. The current flow, or any associated electrical parameter (voltage, current frequency, capacitance, relative dielectric constant, etc.), may be monitored by an associated position feedback. In case of a mechanical failure - where one of the components is damaged- the current flow through the structure would alter and change from its functional calibrated values. At an extreme situation (for example, when a spring is broken), the current would stop completely due to a circuit break in the electrical chain by means of a faulty element.

[090] Fig. 3B illustrates another example scanning unit 104 with a dual axis round MEMS mirror 300. In this example MEMS mirror 300 functions as at least one deflector 114. In one embodiment, MEMS mirror 300 may have a diameter of between about 1 mm to about 5mm. As shown, scanning unit 104 may include four actuators 302 (302A, 302B, 302C, and 302D) each may be at a differing length. In the illustrated example, the current (represented in the figure as the dashed line) flows from contact 304 A to contact 304D, but in other cases current may flow from contact 304A to contact 304B, from contact 304A to contact 304C, from contact 304B to contact 304C, from contact 304B to contact 304D, or from contact 304C to contact 304D. Consistent with some embodiments, a dual axis MEMS mirror may be configured to deflect light in a horizontal direction and in a vertical direction. For example, the angles of deflection of a dual axis MEMS mirror may be between about 0° to 30° in the vertical direction and between about 0° to 50° in the horizontal direction. One skilled in the art will appreciate that the depicted configuration of mirror 300 may have numerous variations and modifications. In one example, at least of deflector 114 may have a dual axis square-shaped mirror or single axis round-shaped mirror. Examples of round and square mirror are depicted in Fig. 3A and 3B as examples only. Any shape may be employed depending on system specifications. In one embodiment, actuators 302 may be incorporated as an integral part of at least of deflector 114, such that power to move MEMS mirror 300 is applied directly towards it. In addition, MEMS mirror 300 may be connected to frame 308 by one or more rigid supporting elements. In another embodiment, at least of deflector 114 may include an electrostatic or electromagnetic MEMS mirror.

[091] As described above, a monostatic scanning LIDAR system utilizes at least a portion of the same optical path for emitting projected light 204 and for receiving reflected light 206. The light beam in the outbound path may be collimated and focused into a narrow beam while the reflections in the return path spread into a larger patch of light, due to dispersion. In one embodiment, scanning unit 104 may have a large reflection area in the return path and asymmetrical deflector 216 that redirects the reflections (i.e., reflected light 206) to sensor 116. In one embodiment, scanning unit 104 may include a MEMS mirror with a large reflection area and negligible impact on the field of view and the frame rate performance. Additional details about the asymmetrical deflector 216 are provided below with reference to Fig. 2D.

[092] In some embodiments (e.g. as exemplified in Fig. 3C), scanning unit 104 may include a deflector array (e.g. a reflector array) with small light deflectors (e.g. mirrors). In one embodiment, implementing light deflector 114 as a group of smaller individual light deflectors working in synchronization may allow light deflector 114 to perform at a high scan rate with larger angles of deflection. The deflector array may essentially act as a large light deflector (e.g. a large mirror) in terms of effective area. The deflector array may be operated using a shared steering assembly configuration that allows sensor 116 to collect reflected photons from substantially the same portion of field of view 120 being concurrently illuminated by light source 112. The term “concurrently” means that the two selected functions occur during coincident or overlapping time periods, either where one begins and ends during the duration of the other, or where a later one starts before the completion of the other.

[093] Fig. 3C illustrates an example of scanning unit 104 with a reflector array 312 having small mirrors. In this embodiment, reflector array 312 functions as at least one deflector 114. Reflector array 312 may include a plurality of reflector units 314 configured to pivot (individually or together) and steer light pulses toward field of view 120. For example, reflector array 312 may be a part of an outbound path of light projected from light source 112. Specifically, reflector array 312 may direct projected light 204 towards a portion of field of view 120. Reflector array 312 may also be part of a return path for light reflected from a surface of an object located within an illumined portion of field of view 120. Specifically, reflector array 312 may direct reflected light 206 towards sensor 116 or towards asymmetrical deflector 216. In one example, the area of reflector array 312 may be between about 75 to about 150 mm ² , where each reflector units 314 may have a width of about 10pm and the supporting structure may be lower than 100 pm.

[094] According to some embodiments, reflector array 312 may include one or more sub-groups of steerable deflectors. Each sub-group of electrically steerable deflectors may include one or more deflector units, such as reflector unit 314. For example, each steerable deflector unit 314 may include at least one of a MEMS mirror, a reflective surface assembly, and an electromechanical actuator. In one embodiment, each reflector unit 314 may be individually controlled by an individual processor (not shown), such that it may tilt towards a specific angle along each of one or two separate axes. Alternatively, reflector array 312 may be associated with a common controller (e.g., processor 118) configured to synchronously manage the movement of reflector units 314 such that at least part of them will pivot concurrently and point in approximately the same direction.

[095] In addition, at least one processor 118 may select at least one reflector unit 314 for the outbound path (referred to hereinafter as “TX Mirror”) and a group of reflector units 314 for the return path (referred to hereinafter as “RX Mirror”). Consistent with the present disclosure, increasing the number of TX Mirrors may increase a reflected photons beam spread. Additionally, decreasing the number of RX Mirrors may narrow the reception field and compensate for ambient light conditions (such as clouds, rain, fog, extreme heat, and other environmental conditions) and improve the signal to noise ratio. Also, as indicated above, the emitted light beam is typically narrower than the patch of reflected light, and therefore can be fully deflected by a small portion of the deflection array. Moreover, it is possible to block light reflected from the portion of the deflection array used for transmission (e.g. the TX mirror) from reaching sensor 116, thereby reducing an effect of internal reflections of the LIDAR system 100 on system operation. In addition, at least one processor 118 may pivot one or more reflector units 314 to overcome mechanical impairments and drifts due, for example, to thermal and gain effects. In an example, one or more reflector units 314 may move differently than intended (frequency, rate, speed etc.) and their movement may be compensated for by electrically controlling the deflectors appropriately.

[096] Fig. 3D illustrates an exemplary LIDAR system 100 that mechanically scans the environment of LIDAR system 100. In this example, LIDAR system 100 may include a motor or other mechanisms for rotating housing 200 about the axis of the LIDAR system 100. Alternatively, the motor (or other mechanism) may mechanically rotate a rigid structure of LIDAR system 100 on which one or more light sources 112 and one or more sensors 116 are installed, thereby scanning the environment. As described above, projecting unit 102 may include at least one light source 112 configured to project light emission. The projected light emission may travel along an outbound path towards field of view 120. Specifically, the projected light emission may be reflected by deflector 114A through an exit aperture 314 when projected light 204 travel towards optional optical window 124. The reflected light emission may travel along a return path from object 208 towards sensing unit 106. For example, the reflected light 206 may be reflected by deflector 114B when reflected light 206 travels towards sensing unit 106. A person skilled in the art would appreciate that a LIDAR system with a rotation mechanism for synchronically rotating one or more light sources or one or more sensors, may use this synchronized rotation instead of (or in addition to) steering an internal light deflector.

[097] In embodiments in which the scanning of field of view 120 is mechanical, the projected light emission may be directed to exit aperture 314 that is part of a wall 316 separating projecting unit 102 from other parts of LIDAR system 100. In some examples, wall 316 can be formed from a transparent material (e.g., glass) coated with a reflective material to form deflector 114B. In this example, exit aperture 314 may correspond to the portion of wall 316 that is not coated by the reflective material. Additionally or alternatively, exit aperture 314 may include a hole or cut-away in the wall 316. Reflected light 206 may be reflected by deflector 114B and directed towards an entrance aperture 318 of sensing unit 106. In some examples, an entrance aperture 318 may include a filtering window configured to allow wavelengths in a certain wavelength range to enter sensing unit 106 and attenuate other wavelengths. The reflections of object 208 from field of view 120 may be reflected by deflector 114B and hit sensor 116. By comparing several properties of reflected light 206 with projected light 204, at least one aspect of object 208 may be determined. For example, by comparing a time when projected light 204 was emitted by light source 112 and a time when sensor 116 received reflected light 206, a distance between object 208 and LIDAR system 100 may be determined. In some examples, other aspects of object 208, such as shape, color, material, etc. may also be determined.

[098] In some examples, the LIDAR system 100 (or part thereof, including at least one light source 112 and at least one sensor 116) may be rotated about at least one axis to determine a three-dimensional map of the surroundings of the LIDAR system 100. For example, the LIDAR system 100 may be rotated about a substantially vertical axis as illustrated by arrow 320 in order to scan field of 120. Although Fig. 3D illustrates that the LIDAR system 100 is rotated clock-wise about the axis as illustrated by the arrow 320, additionally or alternatively, the LIDAR system 100 may be rotated in a counter clockwise direction. In some examples, the LIDAR system 100 may be rotated 360 degrees about the vertical axis. In other examples, the LIDAR system 100 may be rotated back and forth along a sector smaller than 360-degree of the LIDAR system 100. For example, the LIDAR system 100 may be mounted on a platform that wobbles back and forth about the axis without making a complete rotation.

The Sensing Unit

[099] Figs. 4A-4E depict various configurations of sensing unit 106 and its role in LIDAR system 100. Specifically, Fig. 4A is a diagram illustrating an example sensing unit 106 with a detector array, Fig. 4B is a diagram illustrating monostatic scanning using a two- dimensional sensor, Fig. 4C is a diagram illustrating an example of a two-dimensional sensor 116, Fig. 4D is a diagram illustrating a lens array associated with sensor 116, and Fig. 4E includes three diagram illustrating the lens structure. One skilled in the art will appreciate that the depicted configurations of sensing unit 106 are exemplary only and may have numerous alternative variations and modifications consistent with the principles of this disclosure.

[0100] Fig. 4A illustrates an example of sensing unit 106 with detector array 400. In this example, at least one sensor 116 includes detector array 400. LIDAR system 100 is configured to detect objects (e.g., bicycle 208A and cloud 208B) in field of view 120 located at different distances from LIDAR system 100 (could be meters or more). Objects 208 may be a solid object (e.g. a road, a tree, a car, a person), fluid object (e.g. fog, water, atmosphere particles), or object of another type (e.g. dust or a powdery illuminated object). When the photons emitted from light source 112 hit object 208 they either reflect, refract, or get absorbed. Typically, as shown in the figure, only a portion of the photons reflected from object 208A enters optional optical window 124. As each ~15 cm change in distance results in a travel time difference of 1 ns (since the photons travel at the speed of light to and from object 208), the time differences between the travel times of different photons hitting the different objects may be detectable by a time-of- flight sensor with sufficiently quick response.

[0101] Sensor 116 includes a plurality of detection elements 402 for detecting photons of a photonic pulse reflected back from field of view 120. The detection elements may all be included in detector array 400, which may have a rectangular arrangement (e.g. as shown) or any other arrangement. Detection elements 402 may operate concurrently or partially concurrently with each other. Specifically, each detection element 402 may issue detection information for every sampling duration (e.g. every 1 nanosecond). In one example, detector array 400 may be a SiPM (Silicon photomultipliers) which is a solid-state single-photon- sensitive device built from an array of single photon avalanche diodes (SPADs, serving as detection elements 402) on a common silicon substrate. Similar photomultipliers from other, non-silicon materials may also be used. Although a SiPM device works in digital/switching mode, the SiPM is an analog device because all the microcells are read in parallel, making it possible to generate signals within a dynamic range from a single photon to hundreds and thousands of photons detected by the different SPADs. As mentioned above, more than one type of sensor may be implemented (e.g. SiPM and APD). Possibly, sensing unit 106 may include at least one APD integrated into an SiPM array and/or at least one APD detector located next to a SiPM on a separate or common silicon substrate.

[0102] In one embodiment, detection elements 402 may be grouped into a plurality of regions 404. The regions are geometrical locations or environments within sensor 116 (e.g. within detector array 400) - and may be shaped in different shapes (e.g. rectangular as shown, squares, rings, and so on, or in any other shape). While not all of the individual detectors, which are included within the geometrical area of a region 404, necessarily belong to that region, in most cases they will not belong to other regions 404 covering other areas of the sensor 310 - unless some overlap is desired in the seams between regions. As illustrated in Fig. 4A, the regions may be non-overlapping regions 404, but alternatively, they may overlap. Every region may be associated with a regional output circuitry 406 associated with that region. The regional output circuitry 406 may provide a region output signal of a corresponding group of detection elements 402. For example, the region of output circuitry 406 may be a summing circuit, but other forms of combined output of the individual detector into a unitary output (whether scalar, vector, or any other format) may be employed. Optionally, each region 404 is a single SiPM, but this is not necessarily so, and a region may be a sub-portion of a single SiPM, a group of several SiPMs, or even a combination of different types of detectors.

[0103] In the illustrated example, processing unit 108 is located at a separated housing 200B (within or outside) host 210 (e.g. within vehicle 110), and sensing unit 106 may include a dedicated processor 408 for analyzing the reflected light. Alternatively, processing unit 108 may be used for analyzing reflected light 206. It is noted that LIDAR system 100 may be implemented multiple housings in other ways than the illustrated example. For example, light deflector 114 may be located in a different housing than projecting unit 102 and/or sensing module 106. In one embodiment, LIDAR system 100 may include multiple housings connected to each other in different ways, such as: electric wire connection, wireless connection (e.g., RF connection), fiber optics cable, and any combination of the above.

[0104] In one embodiment, analyzing reflected light 206 may include determining a time of flight for reflected light 206, based on outputs of individual detectors of different regions. Optionally, processor 408 may be configured to determine the time of flight for reflected light 206 based on the plurality of regions of output signals. In addition to the time of flight, processing unit 108 may analyze reflected light 206 to determine the average power across an entire return pulse, and the photon distribution/signal may be determined over the return pulse period (“pulse shape”). In the illustrated example, the outputs of any detection elements 402 may not be transmitted directly to processor 408, but rather combined (e.g. summed) with signals of other detectors of the region 404 before being passed to processor 408. However, this is only an example and the circuitry of sensor 116 may transmit information from a detection element 402 to processor 408 via other routes (not via a region output circuitry 406).

[0105] Fig. 4B is a diagram illustrating LIDAR system 100 configured to scan the environment of LIDAR system 100 using a two-dimensional sensor 116. In the example of Fig. 4B, sensor 116 is a matrix of 4X6 detectors 410 (also referred to as “pixels”). In one embodiment, a pixel size may be about Ixlmm. Sensor 116 is two-dimensional in the sense that it has more than one set (e.g. row, column) of detectors 410 in two non-parallel axes (e.g. orthogonal axes, as exemplified in the illustrated examples). The number of detectors 410 in sensor 116 may vary between differing implementations, e.g. depending on the desired resolution, signal to noise ratio (SNR), desired detection distance, and so on. For example, sensor 116 may have anywhere between 5 and 5,000 pixels. In another example (not shown in the figure) Also, sensor 116 may be a one-dimensional matrix (e.g. 1X8 pixels).

[0106] It is noted that each detector 410 may include a plurality of detection elements 402, such as Avalanche Photo Diodes (APD), Single Photon Avalanche Diodes (SPADs), combination of Avalanche Photo Diodes (APD) and Single Photon Avalanche Diodes (SPADs)or detecting elements that measure both the time of flight from a laser pulse transmission event to the reception event and the intensity of the received photons. For example, each detector 410 may include anywhere between 20 and 5,000 SPADs. The outputs of detection elements 402 in each detector 410 may be summed, averaged, or otherwise combined to provide a unified pixel output.

[0107] In the illustrated example, sensing unit 106 may include a two-dimensional sensor 116 (or a plurality of two-dimensional sensors 116), whose field of view is smaller than field of view 120 of LIDAR system 100. In this discussion, field of view 120 (the overall field of view which can be scanned by LIDAR system 100 without moving, rotating or rolling in any direction) is denoted “first FOV 412”, and the smaller FOV of sensor 116 is denoted “second FOV 412” (interchangeably “instantaneous FOV”). The coverage area of second FOV 414 relative to the first FOV 412 may differ, depending on the specific use of LIDAR system 100, and may be, for example, between 0.5% and 50%. In one example, second FOV 412 may be between about 0.05° and 1° elongated in the vertical dimension. Even if LIDAR system 100 includes more than one two-dimensional sensor 116, the combined field of view of the sensors array may still be smaller than the first FOV 412, e.g. by a factor of at least 5, by a factor of at least 10, by a factor of at least 20, or by a factor of at least 50, for example.

[0108] In order to cover first FOV 412, scanning unit 106 may direct photons arriving from different parts of the environment to sensor 116 at different times. In the illustrated monostatic configuration, together with directing projected light 204 towards field of view 120 and when least one light deflector 114 is located in an instantaneous position, scanning unit 106 may also direct reflected light 206 to sensor 116. Typically, at every moment during the scanning of first FOV 412, the light beam emitted by LIDAR system 100 covers part of the environment which is larger than the second FOV 414 (in angular opening) and includes the part of the environment from which light is collected by scanning unit 104 and sensor 116.

[0109] Fig. 4C is a diagram illustrating an example of a two-dimensional sensor 116. In this embodiment, sensor 116 is a matrix of 8X5 detectors 410 and each detector 410 includes a plurality of detection elements 402. In one example, detector 410A is located in the second row (denoted “R2”) and third column (denoted “C3”) of sensor 116, which includes a matrix of 4X3 detection elements 402. In another example, detector 410B located in the fourth row (denoted “R4”) and sixth column (denoted “C6”) of sensor 116 includes a matrix of 3X3 detection elements 402. Accordingly, the number of detection elements 402 in each detector 410 may be constant, or may vary, and differing detectors 410 in a common array may have a different number of detection elements 402. The outputs of all detection elements 402 in each detector 410 may be summed, averaged, or otherwise combined to provide a single pixel-output value. It is noted that while detectors 410 in the example of Fig. 4C are arranged in a rectangular matrix (straight rows and straight columns), other arrangements may also be used, e.g. a circular arrangement or a honeycomb arrangement.

[0110] According to some embodiments, measurements from each detector 410 may enable determination of the time of flight from a light pulse emission event to the reception event and the intensity of the received photons. The reception event may be the result of the light pulse being reflected from object 208. The time of flight may be a timestamp value that represents the distance of the reflecting object to optional optical window 124. Time of flight values may be realized by photon detection and counting methods, such as Time Correlated Single Photon Counters (TCSPC), analog methods for photon detection such as signal integration and qualification (via analog to digital converters or plain comparators) or otherwise.

[0111] In some embodiments and with reference to Fig. 4B, during a scanning cycle, each instantaneous position of at least one light deflector 114 may be associated with a particular portion 122 of field of view 120. The design of sensor 116 enables an association between the reflected light from a single portion of field of view 120 and multiple detectors 410. Therefore, the scanning resolution of LIDAR system may be represented by the number of instantaneous positions (per scanning cycle) times the number of detectors 410 in sensor 116. The information from each detector 410 (i.e., each pixel) represents the basic data element that from which the captured field of view in the three-dimensional space is built. This may include, for example, the basic element of a point cloud representation, with a spatial position and an associated reflected intensity value. In one embodiment, the reflections from a single portion of field of view 120 that are detected by multiple detectors 410 may be returning from different objects located in the single portion of field of view 120. For example, the single portion of field of view 120 may be greater than 50x50 cm at the far field, which can easily include two, three, or more objects partly covered by each other.

[0112] Fig. 4D is a cross cut diagram of a part of sensor 116, in accordance with examples of the presently disclosed subject matter. The illustrated part of sensor 116 includes a part of a detector array 400 which includes four detection elements 402 (e.g., four SPADs, four APDs). Detector array 400 may be a photodetector sensor realized in complementary metal-oxide-semiconductor (CMOS). Each of the detection elements 402 has a sensitive area, which is positioned within a substrate surrounding. While not necessarily so, sensor 116 may be used in a monostatic LiDAR system having a narrow field of view (e.g., because scanning unit 104 scans different parts of the field of view at different times). The narrow field of view for the incoming light beam — if implemented — eliminates the problem of out-of-focus imaging. As exemplified in Fig. 4D, sensor 116 may include a plurality of lenses 422 (e.g., microlenses), each lens 422 may direct incident light toward a different detection element 402 (e.g., toward an active area of detection element 402), which may be usable when out-of- focus imaging is not an issue. Lenses 422 may be used for increasing an optical fill factor and sensitivity of detector array 400, because most of the light that reaches sensor 116 may be deflected toward the active areas of detection elements 402

[0113] Detector array 400, as exemplified in Fig. 4D, may include several layers built into the silicon substrate by various methods (e.g., implant) resulting in a sensitive area, contact elements to the metal layers and isolation elements (e.g., shallow trench implant STI, guard rings, optical trenches, etc.). The sensitive area may be a volumetric element in the CMOS detector that enables the optical conversion of incoming photons into a current flow given an adequate voltage bias is applied to the device. In the case of a APD/SPAD, the sensitive area would be a combination of an electrical field that pulls electrons created by photon absorption towards a multiplication area where a photon induced electron is amplified creating a breakdown avalanche of multiplied electrons.

[0114] A front side illuminated detector (e.g., as illustrated in Fig. 4D) has the input optical port at the same side as the metal layers residing on top of the semiconductor (Silicon). The metal layers are required to realize the electrical connections of each individual photodetector element (e.g., anode and cathode) with various elements such as: bias voltage, quenching/ballast elements, and other photodetectors in a common array. The optical port through which the photons impinge upon the detector sensitive area is comprised of a passage through the metal layer. It is noted that passage of light from some directions through this passage may be blocked by one or more metal layers (e.g., metal layer ML6, as illustrated for the leftmost detector elements 402 in Fig. 4D). Such blockage reduces the total optical light absorbing efficiency of the detector.

[0115] Fig. 4E illustrates three detection elements 402, each with an associated lens 422, in accordance with examples of the presenting disclosed subject matter. Each of the three detection elements of Fig. 4E, denoted 402(1), 402(2), and 402(3), illustrates a lens configuration which may be implemented in associated with one or more of the detecting elements 402 of sensor 116. It is noted that combinations of these lens configurations may also be implemented.

[0116] In the lens configuration illustrated with regards to detection element 402(1), a focal point of the associated lens 422 may be located above the semiconductor surface. Optionally, openings in different metal layers of the detection element may have different sizes aligned with the cone of focusing light generated by the associated lens 422. Such a structure may improve the signal-to-noise and resolution of the array 400 as a whole device. Large metal layers may be important for delivery of power and ground shielding. This approach may be useful, e.g., with a monostatic LiDAR design with a narrow field of view where the incoming light beam is comprised of parallel rays and the imaging focus does not have any consequence to the detected signal.

[0117] In the lens configuration illustrated with regards to detection element 402(2), an efficiency of photon detection by the detection elements 402 may be improved by identifying a sweet spot. Specifically, a photodetector implemented in CMOS may have a sweet spot in the sensitive volume area where the probability of a photon creating an avalanche effect is the highest. Therefore, a focal point of lens 422 may be positioned inside the sensitive volume area at the sweet spot location, as demonstrated by detection elements 402(2). The lens shape and distance from the focal point may take into account the refractive indices of all the elements the laser beam is passing along the way from the lens to the sensitive sweet spot location buried in the semiconductor material.

[0118] In the lens configuration illustrated with regards to the detection element on the right of Fig. 4E, an efficiency of photon absorption in the semiconductor material may be improved using a diffuser and reflective elements. Specifically, a near IR wavelength requires a significantly long path of silicon material in order to achieve a high probability of absorbing a photon that travels through. In a typical lens configuration, a photon may traverse the sensitive area and may not be absorbed into a detectable electron. A long absorption path that improves the probability for a photon to create an electron renders the size of the sensitive area towards less practical dimensions (tens of um for example) for a CMOS device fabricated with typical foundry processes. The rightmost detector element in Fig. 4E demonstrates a technique for processing incoming photons. The associated lens 422 focuses the incoming light onto a diffuser element 424. In one embodiment, light sensor 116 may further include a diffuser located in the gap distant from the outer surface of at least some of the detectors. For example, diffuser 424 may steer the light beam sideways (e.g., as perpendicular as possible) towards the sensitive area and the reflective optical trenches 426. The diffuser is located at the focal point, above the focal point, or below the focal point. In this embodiment, the incoming light may be focused on a specific location where a diffuser element is located. Optionally, detector element 422 is designed to optically avoid the inactive areas where a photon induced electron may get lost and reduce the effective detection efficiency. Reflective optical trenches 426 (or other forms of optically reflective structures) cause the photons to bounce back and forth across the sensitive area, thus increasing the likelihood of detection. Ideally, the photons will get trapped in a cavity consisting of the sensitive area and the reflective trenches indefinitely until the photon is absorbed and creates an electron/hole pair.

[0119] Consistent with the present disclosure, a long path is created for the impinging photons to be absorbed and contribute to a higher probability of detection. Optical trenches may also be implemented in detecting element 422 for reducing cross talk effects of parasitic photons created during an avalanche that may leak to other detectors and cause false detection events. According to some embodiments, a photo detector array may be optimized so that a higher yield of the received signal is utilized, meaning, that as much of the received signal is received and less of the signal is lost to internal degradation of the signal. The photo detector array may be improved by: (a) moving the focal point at a location above the semiconductor surface, optionally by designing the metal layers above the substrate appropriately; (b) by steering the focal point to the most responsive/sensitive area (or “sweet spot”) of the substrate and (c) adding a diffuser above the substrate to steer the signal toward the “sweet spot” and/or adding reflective material to the trenches so that deflected signals are reflected back to the “sweet spot.”

[0120] While in some lens configurations, lens 422 may be positioned so that its focal point is above a center of the corresponding detection element 402, it is noted that this is not necessarily so. In other lens configuration, a position of the focal point of the lens 422 with respect to a center of the corresponding detection element 402 is shifted based on a distance of the respective detection element 402 from a center of the detection array 400. This may be useful in relatively larger detection arrays 400, in which detector elements further from the center receive light in angles which are increasingly off-axis. Shifting the location of the focal points (e.g., toward the center of detection array 400) allows correcting for the incidence angles. Specifically, shifting the location of the focal points (e.g., toward the center of detection array 400) allows correcting for the incidence angles while using substantially identical lenses 422 for all detection elements, which are positioned at the same angle with respect to a surface of the detector.

[0121] Adding an array of lenses 422 to an array of detection elements 402 may be useful when using a relatively small sensor 116 which covers only a small part of the field of view because in such a case, the reflection signals from the scene reach the detectors array 400 from substantially the same angle, and it is, therefore, easy to focus all the light onto individual detectors. It is also noted, that in one embodiment, lenses 422 may be used in LIDAR system 100 for favoring about increasing the overall probability of detection of the entire array 400 (preventing photons from being “wasted” in the dead area between detectors/sub-detectors) at the expense of spatial distinctiveness. This embodiment is in contrast to prior art implementations such as CMOS RGB camera, which prioritize spatial distinctiveness (i.e., light that propagates in the direction of detection element A is not allowed to be directed by the lens toward detection element B, that is, to “bleed” to another detection element of the array). Optionally, sensor 116 includes an array of lens 422, each being correlated to a corresponding detection element 402, while at least one of the lenses 422 deflects light which propagates to a first detection element 402 toward a second detection element 402 (thereby it may increase the overall probability of detection of the entire array).

[0122] Specifically, consistent with some embodiments of the present disclosure, light sensor 116 may include an array of light detectors (e.g., detector array 400), each light detector (e.g., detector 410) being configured to cause an electric current to flow when light passes through an outer surface of a respective detector. In addition, light sensor 116 may include at least one micro-lens configured to direct light toward the array of light detectors, the at least one micro-lens having a focal point. Light sensor 116 may further include at least one layer of conductive material interposed between the at least one micro-lens and the array of light detectors and having a gap therein to permit light to pass from the at least one microlens to the array, the at least one layer being sized to maintain a space between the at least one micro- lens and the array to cause the focal point (e.g., the focal point may be a plane) to be located in the gap, at a location spaced from the detecting surfaces of the array of light detectors.

[0123] In related embodiments, each detector may include a plurality of Single Photon Avalanche Diodes (SPADs) or a plurality of Avalanche Photo Diodes (APD). The conductive material may be a multi-layer metal constriction, and the at least one layer of conductive material may be electrically connected to detectors in the array. In one example, the at least one layer of conductive material includes a plurality of layers. In addition, the gap may be shaped to converge from the at least one micro-lens toward the focal point, and to diverge from a region of the focal point toward the array. In other embodiments, light sensor 116 may further include at least one reflector adjacent each photo detector. In one embodiment, a plurality of micro-lenses may be arranged in a lens array and the plurality of detectors may be arranged in a detector array. In another embodiment, the plurality of micro-lenses may include a single lens configured to project light to a plurality of detectors in the array.

[0124] Referring by way of a nonlimiting example to Figs. 2E, 2F and 2G, it is noted that the one or more sensors 116 of system 100 may receive light from a scanning deflector 114 or directly from the FOV without scanning. Even if light from the entire FOV arrives to the at least one sensor 116 at the same time, in some implementations the one or more sensors 116 may sample only parts of the FOV for detection output at any given time. For example, if the illumination of projection unit 102 illuminates different parts of the FOV at different times (whether using a deflector 114 and/or by activating different light sources 112 at different times), light may arrive at all of the pixels or sensors 116 of sensing unit 106, and only pixels/sensors which are expected to detect the LIDAR illumination may be actively collecting data for detection outputs. This way, the rest of the pixels/sensors do not unnecessarily collect ambient noise. Referring to the scanning - in the outbound or in the inbound directions - it is noted that substantially different scales of scanning may be implemented. For example, in some implementations the scanned area may cover l%o or 0.1%o of the FOV, while in other implementations the scanned area may cover 10% or 25% of the FOV. All other relative portions of the FOV values may also be implemented, of course. The Processing Unit

[0125] Figs. 5A-5C depict different functionalities of processing units 108 in accordance with some embodiments of the present disclosure. Specifically, Fig. 5A is a diagram illustrating emission patterns in a single frame-time for a single portion of the field of view, Fig. 5B is a diagram illustrating emission scheme in a single frame-time for the whole field of view, and. Fig. 5C is a diagram illustrating the actual light emission projected towards field of view during a single scanning cycle.

[0126] Fig. 5A illustrates four examples of emission patterns in a single frame-time for a single portion 122 of field of view 120 associated with an instantaneous position of at least one light deflector 114. Consistent with embodiments of the present disclosure, processing unit 108 may control at least one light source 112 and light deflector 114 (or coordinate the operation of at least one light source 112 and at least one light deflector 114) in a manner enabling light flux to vary over a scan of field of view 120. Consistent with other embodiments, processing unit 108 may control only at least one light source 112 and light deflector 114 may be moved or pivoted in a fixed predefined pattern.

[0127] Diagrams A-D in Fig. 5 A depict the power of light emitted towards a single portion 122 of field of view 120 over time. In Diagram A, processor 118 may control the operation of light source 112 in a manner such that during scanning of field of view 120 an initial light emission is projected toward portion 122 of field of view 120. When projecting unit 102 includes a pulsed-light light source, the initial light emission may include one or more initial pulses (also referred to as “pilot pulses”). Processing unit 108 may receive from sensor 116 pilot information about reflections associated with the initial light emission. In one embodiment, the pilot information may be represented as a single signal based on the outputs of one or more detectors (e.g. one or more SPADs, one or more APDs, one or more SiPMs, etc.) or as a plurality of signals based on the outputs of multiple detectors. In one example, the pilot information may include analog and/or digital information. In another example, the pilot information may include a single value and/or a plurality of values (e.g. for different times and/or parts of the segment).

[0128] Based on information about reflections associated with the initial light emission, processing unit 108 may be configured to determine the type of subsequent light emission to be projected towards portion 122 of field of view 120. The determined subsequent light emission for the particular portion of field of view 120 may be made during the same scanning cycle (i.e., in the same frame) or in a subsequent scanning cycle (i.e., in a subsequent frame).

[0129] In Diagram B, processor 118 may control the operation of light source 112 in a manner such that during scanning of field of view 120 light pulses in different intensities are projected towards a single portion 122 of field of view 120. In one embodiment, LIDAR system 100 may be operable to generate depth maps of one or more different types, such as any one or more of the following types: point cloud model, polygon mesh, depth image (holding depth information for each pixel of an image or of a 2D array), or any other type of 3D model of a scene. The sequence of depth maps may be a temporal sequence, in which different depth maps are generated at a different time. Each depth map of the sequence associated with a scanning cycle (interchangeably “frame”) may be generated within the duration of a corresponding subsequent frame-time. In one example, a typical frame-time may last less than a second. In some embodiments, LIDAR system 100 may have a fixed frame rate (e.g. 10 frames per second, 25 frames per second, 50 frames per second) or the frame rate may be dynamic. In other embodiments, the frame-times of different frames may not be identical across the sequence. For example, LIDAR system 100 may implement a 10 frames- per- second rate that includes generating a first depth map in 100 milliseconds (the average), a second frame in 92 milliseconds, a third frame at 142 milliseconds, and so on.

[0130] In Diagram C, processor 118 may control the operation of light source 112 in a manner such that during scanning of field of view 120 light pulses associated with different durations are projected towards a single portion 122 of field of view 120. In one embodiment, LIDAR system 100 may be operable to generate a different number of pulses in each frame. The number of pulses may vary between 0 to 32 pulses (e.g., 1, 5, 12, 28, or more pulses) and may be based on information derived from previous emissions. The time between light pulses may depend on desired detection range and can be between 500ns and 5000ns. In one example, processing unit 108 may receive from sensor 116 information about reflections associated with each light-pulse. Based on the information (or the lack of information), processing unit 108 may determine if additional light pulses are needed. It is noted that the durations of the processing times and the emission times in diagrams A-D are not in-scale. Specifically, the processing time may be substantially longer than the emission time. In diagram D, projecting unit 102 may include a continuous-wave light source. In one embodiment, the initial light emission may include a period of time where light is emitted and the subsequent emission may be a continuation of the initial emission, or there may be a discontinuity. In one embodiment, the intensity of the continuous emission may change over time.

[0131] Consistent with some embodiments of the present disclosure, the emission pattern may be determined per each portion of field of view 120. In other words, processor 118 may control the emission of light to allow differentiation in the illumination of different portions of field of view 120. In one example, processor 118 may determine the emission pattern for a single portion 122 of field of view 120, based on detection of reflected light from the same scanning cycle (e.g., the initial emission), which makes LIDAR system 100 extremely dynamic. In another example, processor 118 may determine the emission pattern for a single portion 122 of field of view 120, based on detection of reflected light from a previous scanning cycle. The differences in the patterns of the subsequent emissions may result from determining different values for light-source parameters for the subsequent emission, such as any one of the following: a. Overall energy of the subsequent emission. b. Energy profile of the subsequent emission. c. A number of light-pulse-repetition per frame. d. Light modulation characteristics such as duration, rate, peak, average power, and pulse shape. e. Wave properties of the subsequent emission, such as polarization, wavelength, etc.

[0132] Consistent with the present disclosure, the differentiation in the subsequent emissions may be put to different uses. In one example, it is possible to limit emitted power levels in one portion of field of view 120 where safety is a consideration, while emitting higher power levels (thus improving signal-to-noise ratio and detection range) for other portions of field of view 120. This is relevant for eye safety, but may also be relevant for skin safety, safety of optical systems, safety of sensitive materials, and more. In another example, it is possible to direct more energy towards portions of field of view 120 where it will be of greater use (e.g. regions of interest, further distanced targets, low reflection targets, etc.) while limiting the lighting energy to other portions of field of view 120 based on detection results from the same frame or previous frame. It is noted that processing unit 108 may process detected signals from a single instantaneous field of view several times within a single scanning frame time; for example, subsequent emission may be determined upon after every pulse emitted, or after a number of pulses emitted.

[0133] Fig. 5B illustrates three examples of emission schemes in a single frame-time for field of view 120. Consistent with embodiments of the present disclosure, at least on processing unit 108 may use obtained information to dynamically adjust the operational mode of LIDAR system 100 and/or determine values of parameters of specific components of LIDAR system 100. The obtained information may be determined from processing data captured in field of view 120, or received (directly or indirectly) from host 210. Processing unit 108 may use the obtained information to determine a scanning scheme for scanning the different portions of field of view 120. The obtained information may include a current light condition, a current weather condition, a current driving environment of the host vehicle, a current location of the host vehicle, a current trajectory of the host vehicle, a current topography of road surrounding the host vehicle, or any other condition or object detectable through light reflection. In some embodiments, the determined scanning scheme may include at least one of the following: (a) a designation of portions within field of view 120 to be actively scanned as part of a scanning cycle, (b) a projecting plan for projecting unit 102 that defines the light emission profile at different portions of field of view 120; (c) a deflecting plan for scanning unit 104 that defines, for example, a deflection direction, frequency, and designating idle elements within a reflector array; and (d) a detection plan for sensing unit 106 that defines the detectors sensitivity or responsivity pattern.

[0134] In addition, processing unit 108 may determine the scanning scheme at least partially by obtaining an identification of at least one region of interest within the field of view 120 and at least one region of non-interest within the field of view 120. In some embodiments, processing unit 108 may determine the scanning scheme at least partially by obtaining an identification of at least one region of high interest within the field of view 120 and at least one region of lower-interest within the field of view 120. The identification of the at least one region of interest within the field of view 120 may be determined, for example, from processing data captured in field of view 120, based on data of another sensor (e.g. camera, GPS), received (directly or indirectly) from host 210, or any combination of the above. In some embodiments, the identification of at least one region of interest may include identification of portions, areas, sections, pixels, or objects within field of view 120 that are important to monitor. Examples of areas that may be identified as regions of interest may include, crosswalks, moving objects, people, nearby vehicles or any other environmental condition or object that may be helpful in vehicle navigation. Examples of areas that may be identified as regions of non-interest (or lower-interest) may be static (non-moving) far-away buildings, a skyline, an area above the horizon and objects in the field of view. Upon obtaining the identification of at least one region of interest within the field of view 120, processing unit 108 may determine the scanning scheme or change an existing scanning scheme. Further to determining or changing the light-source parameters (as described above), processing unit 108 may allocate detector resources based on the identification of the at least one region of interest. In one example, to reduce noise, processing unit 108 may activate detectors 410 where a region of interest is expected and disable detectors 410 where regions of non-interest are expected. In another example, processing unit 108 may change the detector sensitivity, e.g., increasing sensor sensitivity for long range detection where the reflected power is low.

[0135] Diagrams A-C in Fig. 5B depict examples of different scanning schemes for scanning field of view 120. Each square in field of view 120 represents a different portion 122 associated with an instantaneous position of at least one light deflector 114. Legend 500 details the level of light flux represented by the filling pattern of the squares. Diagram A depicts a first scanning scheme in which all of the portions have the same importance/priority and a default light flux is allocated to them. The first scanning scheme may be utilized in a start-up phase or periodically interleaved with another scanning scheme to monitor the whole field of view for unexpected/new objects. In one example, the light source parameters in the first scanning scheme may be configured to generate light pulses at constant amplitudes. Diagram B depicts a second scanning scheme in which a portion of field of view 120 is allocated with high light flux while the rest of field of view 120 is allocated with default light flux and low light flux. The portions of field of view 120 that are the least interesting may be allocated with low light flux. Diagram C depicts a third scanning scheme in which a compact vehicle and a bus (see silhouettes) are identified in field of view 120. In this scanning scheme, the edges of the vehicle and bus may be tracked with high power and the central mass of the vehicle and bus may be allocated with less light flux (or no light flux). Such light flux allocation enables concentration of more of the optical budget on the edges of the identified objects and less on their center which have less importance. [0136] Fig. 5C illustrating the emission of light towards field of view 120 during a single scanning cycle. In the depicted example, field of view 120 is represented by an 8 X 9 matrix, where each of the 72 cells corresponds to a separate portion 122 associated with a different instantaneous position of at least one light deflector 114. In this exemplary scanning cycle, each portion includes one or more white dots that represent the number of light pulses projected toward that portion, and some portions include black dots that represent reflected light from that portion detected by sensor 116. As shown, field of view 120 is divided into three sectors: sector I on the right side of field of view 120, sector II in the middle of field of view 120, and sector III on the left side of field of view 120. In this exemplary scanning cycle, sector I was initially allocated with a single light pulse per portion; sector II, previously identified as a region of interest, was initially allocated with three light pulses per portion; and sector III was initially allocated with two light pulses per portion. Also as shown, scanning of field of view 120 reveals four objects 208: two free-form objects in the near field (e.g., between 5 and 50 meters), a rounded-square object in the mid field (e.g., between 50 and 150 meters), and a triangle object in the far field (e.g., between 150 and 500 meters). While the discussion of Fig. 5C uses number of pulses as an example of light flux allocation, it is noted that light flux allocation to different parts of the field of view may also be implemented in other ways such as: pulse duration, pulse angular dispersion, wavelength, instantaneous power, photon density at different distances from light source 112, average power, pulse power intensity, pulse width, pulse repetition rate, pulse sequence, pulse duty cycle, wavelength, phase, polarization, and more. The illustration of the light emission as a single scanning cycle in Fig. 5C demonstrates different capabilities of LIDAR system 100. In a first embodiment, processor 118 is configured to use two light pulses to detect a first object (e.g., the rounded-square object) at a first distance, and to use three light pulses to detect a second object (e.g., the triangle object) at a second distance greater than the first distance. In a second embodiment, processor 118 is configured to allocate more light to portions of the field of view where a region of interest is identified. Specifically, in the present example, sector II was identified as a region of interest and accordingly it was allocated with three light pulses while the rest of field of view 120 was allocated with two or less light pulses. In a third embodiment, processor 118 is configured to control light source 112 in a manner such that only a single light pulse is projected toward to portions Bl, B2, and Cl in Fig. 5C, although they are part of sector III that was initially allocated with two light pulses per portion. This occurs because the processing unit 108 detected an object in the near field based on the first light pulse. Allocation of less than maximal amount of pulses may also be a result of other considerations. For examples, in at least some regions, detection of object at a first distance (e.g. a near field object) may result in reducing an overall amount of light emitted to this portion of field of view 120.

[0137] Additional details and examples on different components of LIDAR system 100 and their associated functionalities are included in Applicant’s United States Patent Application No. 15/391,916 filed December 28, 2016; Applicant’s United States Patent Application No. 15/393,749 filed December 29, 2016; Applicant’s United States Patent Application No. 15/393,285 filed December 29, 2016; and Applicant’s United States Patent Application No. 15/393,593 filed December 29, 2016, which are incorporated herein by reference in their entirety.

Example Implementation: Vehicle

[0138] Figs. 6A-6C illustrate the implementation of LIDAR system 100 in a vehicle (e.g., vehicle 110). Any of the aspects of LIDAR system 100 described above or below may be incorporated into vehicle 110 to provide a range-sensing vehicle. Specifically, in this example, LIDAR system 100 integrates multiple scanning units 104 and potentially multiple projecting units 102 in a single vehicle. In one embodiment, a vehicle may take advantage of such a LIDAR system to improve power, range, and accuracy in the overlap zone and beyond it, as well as redundancy in sensitive parts of the FOV (e.g. the forward movement direction of the vehicle). As shown in Fig. 6A, vehicle 110 may include a first processor 118A for controlling the scanning of field of view 120A, a second processor 118B for controlling the scanning of field of view 120B, and a third processor 118C for controlling synchronization of scanning the two fields of view. In one example, processor 118C may be the vehicle controller and may have a shared interface between first processor 118A and second processor 118B. The shared interface may enable an exchanging of data at intermediate processing levels and a synchronization of scanning of the combined field of view in order to form an overlap in the temporal and/or spatial space. In one embodiment, the data exchanged using the shared interface may be: (a) time of flight of received signals associated with pixels in the overlapped field of view and/or in its vicinity; (b) laser steering position status; (c) detection status of objects in the field of view.

[0139] Fig. 6B illustrates overlap region 600 between field of view 120 A and field of view 120B. In the depicted example, the overlap region is associated with 24 portions 122 from field of view 120 A and 24 portions 122 from field of view 120B. Given that the overlap region is defined and known by processors 118 A and 118B , each processor may be designed to limit the amount of light emitted in overlap region 600 in order to conform with an eye safety limit that spans multiple source lights, or for other reasons such as maintaining an optical budget. In addition, processors 118A and 118B may avoid interferences between the light emitted by the two light sources by loose synchronization between the scanning unit 104A and scanning unit 104B, and/or by control of the laser transmission timing, and/or the detection circuit enabling timing.

[0140] Fig. 6C illustrates how overlap region 600 between field of view 120A and field of view 120B may be used to increase the detection distance of vehicle 110. Consistent with the present disclosure, two or more light sources 112 projecting their nominal light emission into the overlap zone may be leveraged to increase the effective detection range. The term “detection range” may include an approximate distance from vehicle 110 at which LIDAR system 100 can clearly detect an object. In one embodiment, the maximum detection range of LIDAR system 100 is about 300 meters, about 400 meters, or about 500 meters. For example, for a detection range of 200 meters, LIDAR system 100 may detect an object located 200 meters (or less) from vehicle 110 at more than 95%, more than 99%, more than 99.5% of the times. Even when the object’s reflectivity may be less than 50% (e.g., less than 20%, less than 10%, or less than 5%). In addition, LIDAR system 100 may have less than 1% false alarm rate. In one embodiment, light from projected from two light sources that are collocated in the temporal and spatial space can be utilized to improve SNR and therefore increase the range and/or quality of service for an object located in the overlap region. Processor 118C may extract high-level information from the reflected light in field of view 120A and 120B. The term “extracting information” may include any process by which information associated with objects, individuals, locations, events, etc., is identified in the captured image data by any means known to those of ordinary skill in the art. In addition, processors 118A and 118B may share the high-level information, such as objects (road delimiters, background, pedestrians, vehicles, etc.), and motion vectors, to enable each processor to become alert to the peripheral regions about to become regions of interest. For example, a moving object in field of view 120A may be determined to soon be entering field of view 120B.

Example Implementation: Surveillance System

[0141] Fig. 6D illustrates the implementation of LIDAR system 100 in a surveillance system. As mentioned above, LIDAR system 100 may be fixed to a stationary object 650 that may include a motor or other mechanism for rotating the housing of the LIDAR system 100 to obtain a wider field of view. Alternatively, the surveillance system may include a plurality of LIDAR units. In the example depicted in Fig. 6D, the surveillance system may use a single rotatable LIDAR system 100 to obtain 3D data representing field of view 120 and to process the 3D data to detect people 652, vehicles 654, changes in the environment, or any other form of security-significant data. [0142] Consistent with some embodiment of the present disclosure, the 3D data may be analyzed to monitor retail business processes. In one embodiment, the 3D data may be used in retail business processes involving physical security (e.g., detection of: an intrusion within a retail facility, an act of vandalism within or around a retail facility, unauthorized access to a secure area, and suspicious behavior around cars in a parking lot). In another embodiment, the 3D data may be used in public safety (e.g., detection of: people slipping and falling on store property, a dangerous liquid spill or obstruction on a store floor, an assault or abduction in a store parking lot, an obstruction of a fire exit, and crowding in a store area or outside of the store). In another embodiment, the 3D data may be used for business intelligence data gathering (e.g., tracking of people through store areas to determine, for example, how many people go through, where they dwell, how long they dwell, how their shopping habits compare to their purchasing habits).

[0143] Consistent with other embodiments of the present disclosure, the 3D data may be analyzed and used for traffic enforcement. Specifically, the 3D data may be used to identify vehicles traveling over the legal speed limit or some other road legal requirement. In one example, LIDAR system 100 may be used to detect vehicles that cross a stop line or designated stopping place while a red traffic light is showing. In another example, LIDAR system 100 may be used to identify vehicles traveling in lanes reserved for public transportation. In yet another example, LIDAR system 100 may be used to identify vehicles turning in intersections where specific turns are prohibited on red.

[0144] It should be noted that while examples of various disclosed embodiments have been described above and below with respect to a control unit that controls scanning of a deflector, the various features of the disclosed embodiments are not limited to such systems. Rather, the techniques for allocating light to various portions of a LIDAR FOV may be applicable to type of light-based sensing system (LIDAR or otherwise) in which there may be a desire or need to direct different amounts of light to different portions of field of view. In some cases, such light allocation techniques may positively impact detection capabilities, as described herein, but other advantages may also result.

[0145] It should also be noted that various sections of the disclosure and the claims may refer to various components or portions of components (e.g., light sources, sensors, sensor pixels, field of view portions, field of view pixels, etc.) using such terms as “first,” “second,” “third,” etc. These terms are used only to facilitate the description of the various disclosed embodiments and are not intended to be limiting or to indicate any necessary correlation with similarly named elements or components in other embodiments. For example, characteristics described as associated with a “first sensor” in one described embodiment in one section of the disclosure may or may not be associated with a “first sensor” of a different embodiment described in a different section of the disclosure.

[0146] It is noted that LIDAR system 100, or any of its components, may be used together with any of the particular embodiments and methods disclosed below. Nevertheless, the particular embodiments and methods disclosed below are not necessarily limited to LIDAR system 100, and may possibly be implemented in or by other systems (such as but not limited to other LIDAR systems, other electrooptical systems, other optical systems, etc. - whichever is applicable). Also, while system 100 is described relative to an exemplary vehicle-based LIDAR platform, system 100, any of its components, and any of the processes described herein may be applicable to LID AR systems disposed on other platform types. Likewise, the embodiments and processes disclosed below may be implemented on or by LID AR systems ( or other systems such as other electro-optical systems etc.) which are installed on systems disposed on platforms other than vehicles, or even regardless of any specific platform.

Example Implementation: LIDAR System with Automatic Correction for Pitch and Roll Alignment

[0147] The field of view (FOV) of a LIDAR system defines a three-dimensional volume extending from the LIDAR system. The LIDAR system projects laser light within this volume, and based on receipt of reflected laser light signals, the LIDAR system can detect objects, surfaces, etc. within the FOV and determine range information relative to the detected objects and surfaces. Thus, the LIDAR FOV effectively corresponds to the working zone of a particular LIDAR system. If there is an interest in object detection and ranging within a certain region of an environment, the LIDAR system may be oriented such that its FOV overlaps with and/or encompasses that region.

[0148] In some cases, a LIDAR system may be deployed on a host vehicle such that its FOV extends over a vertical range sufficient to detect objects both close to the vehicle as well as more distantly located objects. For example, the positioning of the LIDAR system relative to a host vehicle, in combination with a vertical scan (or working) range of the LIDAR system, may result in a capability of detecting and ranging of objects residing on a ground plane and located within two meters, one meter, 50 centimeters, 25 centimeters, etc. forward, to the sides, to the rear, or with respect to corners of the host vehicle. At the same time, the LIDAR system may be capable of detecting and ranging more distant objects located in a solid angle including a horizon, in a region below the horizon, and/or in a region above the horizon. [0149] In some cases, a LIDAR system may be configured with an FOV having a horizontal range of up to 360 degrees. Such a LIDAR system may be capable of providing a full 360-degree scan of an environment of the LIDAR system. In other cases, a LIDAR system may have an FOV with a more limited horizontal range (e.g., 45 degrees, 90 degrees, 120 degrees, 180 degrees, etc.). In such cases, multiple LIDAR systems may be deployed to provide a desired level of coverage relative to an environment in which the LIDAR systems operate. For example, a LIDAR system may be deployed on a host vehicle such that its FOV includes a region forward of the host. Other LIDAR systems may also be deployed on the host vehicle such that the respective fields of view of the deployed LIDAR systems encompass regions to the sides of the host vehicle, a region to the rear of the host vehicle, regions centered about rays extending from comers of the host vehicle, etc.

[0150] In addition to the horizontal scan range of a LIDAR system, the FOV of a LIDAR system may also be defined by its vertical scan range (e.g., +/- 5 degrees, 10 degrees, 15 degrees, 20 degrees, etc.). The vertical scan range of a LIDAR system may affect how much of the environment above and/or below a horizon is accessible to the LIDAR system.

[0151] Many systems may rely upon the information generated by a LIDAR system. For example, autonomous vehicle navigation systems, advanced driver assist systems, etc. may all rely upon LIDAR system outputs that may include object detections, object identification/classification, object range information, object envelope location and range, etc. To provide the information required by various different systems, a LIDAR system may be implemented such that its FOV encompasses particular regions of an environment from which object detection and ranging information is desired. As noted above, the horizontal and vertical scan limits of a LIDAR system will impact the extent of coverage of a LIDAR system FOV relative to an environment. Additionally, the orientation of a LIDAR system will also impact the location of the LIDAR FOV relative to the environment. Thus, in addition to selecting the vertical and horizontal scan limits of a LIDAR system to the size of the LIDAR FOV, the orientation of the LIDAR system (e.g., relative to a host vehicle on which the LIDAR system is deployed) may also be selected such that the LIDAR FOV overlaps with a desired region of the environment.

[0152] Changes in orientation of the LIDAR system can lead to undesired displacement of the LIDAR FOV relative to the environment. For example, tilting a LIDAR system upward from an intended orientation can exclude from the working range of the LIDAR system regions close to a host vehicle where object detections may be desirable. Moreover, such an upward tilt of a LIDAR system can result in increased scanning of regions significantly above a horizon (e.g., sky) where few objects of interest may be located. Changes in LIDAR system orientation, therefore, can reduce LIDAR scanning resources dedicated to regions of interest while increasing the scanning resources dedicated to regions of less interest.

[0153] Many sources may cause undesirable variations in LIDAR system orientation. For example, during manufacture or installation of a LIDAR system on a host vehicle, the LIDAR system may be misaligned relative to the host vehicle (e.g., disposed at a relative alignment different from a specified, intended relative alignment). During operation, vibration, impacts, or other mechanical effects may cause unintended changes in LIDAR system orientation relative to a host vehicle and the environment. Further, variation in host vehicle pitch resulting from changes in weight distribution (e.g., due to the presence of passengers in a rear seat, the absence of passengers in a rear seat, additions of items to a vehicle trunk, etc.), changes in wind drag (e.g., due to the speed and relative direction of wind impacting a host vehicle, the speed of the host vehicle, etc.), or other potential sources may result in undesired shifts of the LIDAR FOV relative to the environment.

[0154] Importantly, even small changes in LIDAR system orientation can have significant effect on the position of the LIDAR FOV relative to the environment. For example, an increase in LIDAR system pitch of just five degrees may result in 10 cm to one meter (or more) of lost working region adjacent to a host vehicle. Such a change in pitch will also shift the LIDAR FOV by 13 meters at a range of 150 meters from the LIDAR system. Even a change in pitch of just 0.5 degrees can result in shifts of 1 meter or more to the relative orientation of the LIDAR FOV and the environment. Such shifts in pitch, which are well within the range of expected effects caused by changes in vehicle weight distribution, wind drag, etc., can have significant impact on the operation of systems that rely on information generated by a LIDAR system. Relative changes in orientation between a LIDAR system and an area of interest in the environment may also result from variations in road topology. For example, approaching an incline, there may be an interest in shifting the LIDAR system FOV upward relative to the environment to capture information associated with a top of the incline/upward shifted horizon, etc. Similarly, approaching a decline, there may be an interest in shifting the LIDAR system FOV downward relative to the environment to capture information associated with a bottom of the incline, etc.

[0155] Thus, there is a need for systems that can detect changes in relative orientation between a LIDAR FOV and an environment and to automatically correct for such changes. As discussed in more detail below, such a correction may include enlargement of the LIDAR FOV. Such a correction, however, can lead to increased scan times, decreased resolution, non-optimal power management, etc. Other solutions may include shifting of one or more scan limits of the LIDAR system to actively change the relative orientation between the LIDAR system FOV and the surrounding environment.

[0156] Fig. 7 diagrammatically illustrates one system for characterizing a host vehicle 110’ s orientation (and the orientations of one or more associated LIDAR systems deployed on the host vehicle) relative to an environment. For example, host vehicle or LIDAR system orientation can be described by a pitch angle (0), a yaw angle (\|/), and a roll angle (cp). Specifically, in the example shown in Fig. 7, in a Cartesian coordinate system, the x-axis and y-axis can be used to define a surface parallel to the driving surface (e.g., road surface), with the x-axis being along the longitudinal direction of vehicle 110. Moreover, the z-axis is along a direction perpendicular to the driving surface and plane including the x-axis and y-axis. In this example, the pitch angle (0) is a rotation angle around the y-axis. The yaw angle (\|/) is a rotation angle around the z-axis, and the roll angle (cp) is a rotation angle around the x-axis.

[0157] In many cases, a LIDAR system may be installed on a host vehicle in a fixed orientation relative to the host vehicle. For example, taking into account an available field of view provided by a LIDAR system and a desired region of an environment to be scanned, the LIDAR system may be rigidly secured to a host vehicle at a specified orientation (e.g., an orientation provided by a mounting fixture, dedicated interface, etc.). In such an example, the orientation of the LIDAR system FOV relative to the environment will vary with changes in host vehicle orientation (e.g., pitch, roll, yaw). As an example, Fig. 8A illustrates LIDAR system 100 having an FOV centered about a forward-looking direction of a vehicle 110, such that the LIDAR system FOV encompasses a region of the vehicle environment forward of the vehicle 110.

[0158] FIG. 8B illustrates an example in which a pitch of vehicle 110 changes due to, for example, an unbalanced load (e.g., heavy load in the trunk), passengers or cargo in a rear seat of the vehicle, a change in car suspension, unbalanced air pressure in tires, etc. As a result of the change in the pitch of vehicle 110, a corresponding change in pitch of LIDAR system 100 also occurs, which translates to a shift in the orientation of the LIDAR system FOV 120 relative to the environment. Such a shift may include a rotation of the LIDAR FOV in the pitch direction. In this example, the change in pitch of the vehicle/LIDAR system is represented by A0. As shown in Figs. 8A and 8B, the FOV 120 is diagrammatically represented as a two-dimensional slice of the FOV volume. It should be noted, however, that the FOV 120 originates at the LIDAR system and includes a volume extending from the LIDAR system up to and beyond the illustrated slice of the FOV 120 shown in Figs. 8A and 8B. Thus, it can be understood that even a small change in pitch angle, A0, of the LIDAR system may result in shifts in the relative orientation between the FOV 120 and the environment, especially as distance from the LIDAR system increases.

[0159] Changes in orientation between the LIDAR system FOV and the environment may degrade LIDAR system performance such that the LIDAR system may no longer operate according to specification. For example, certain systems may rely upon the LIDAR system to capture information relative to a region of the environment that is no longer fully encompassed by the LIDAR FOV due to the misaligned pitch. For example, as illustrated by Fig. 9A, a LIDAR system 100 may be mounted on vehicle 110 such that the LIDAR system FOV 120 encompasses a particular region of the environment 902 (e.g., a region that includes an area just forward of vehicle 110 up to or just above a horizon). A change in the pitch of the vehicle 110 may cause the FOV 120 to shift up or down relative to region 902. For example, as shown in Fig. 9B, a positive change in pitch angle of vehicle 110 (as shown in Fig. 8B) may cause FOV 120 to shift upward relative to region 902, such that a portion of region 902 is no longer encompassed by FOV 120. This may be problematic, especially if the excluded region is likely to contain target objects of interest to one or more systems of vehicle 110.

[0160] Similarly, a change in the yaw of vehicle 110 and LIDAR system 100 may cause the FOV 120 to shift right or left relative to the environment. For example, as shown in Fig. 10A, the FOV 120 of LIDAR system 100 may initially cover a region 1002 in an environment of vehicle 110. As a result of a change in yaw angle of the LIDAR system relative to the environment, however, the FOV 120 may no longer fully encompass region 1002, as shown in Fig. 10B. Likewise, a change in roll angle of the LIDAR system relative to the environment may cause FOV 120 to rotate relative to the environment. For example, as shown in Fig. 11 A, while FOV 120 of LIDAR system 100 may initially cover a region 1102 of the environment, a change in roll angle of the LIDAR system may cause a shift in the orientation of the FOV 120 relative to the environment such that region 1102 is no longer encompassed by FOV 120, as shown in Fig. 11B. The disclosed embodiments are aimed at detecting such changes in orientation of the LIDAR system relative to the environment and actively adjusting a scan range of the LIDAR system to selectively vary the orientation of the LIDAR system FOV relative to the environment of the LIDAR system.

[0161] Fig. 12 is a flow chart of a method 1200 for detecting a misalignment of a LIDAR system (e.g., the existence of any difference between an intended orientation of a LIDAR system and an actual orientation of the LIDAR system relative to the environment, the host vehicle, or both) and actively adjusting the LIDAR FOV to compensate for the detected misalignment. Consistent with the disclosed embodiments, method 1200 may be performed by LIDAR system 100 in Fig. 1A. As shown in Fig. 12, method 1200 includes at least the following steps 1202-1206.

[0162] In step 1202, processing unit 108 may control projecting unit 102, scanning unit 104, and sensing unit 106 to capture one or more LIDAR frames. Each LIDAR frame may include the generation of a point cloud representing a scene surrounding vehicle 110. In step 1204, processing unit 108 may analyze the one or more LIDAR frames to determine a pitch, yaw, and/or roll misalignment of LIDAR system 100 relative to the environment. In step 1206, processing unit 108 may control one or more of projecting unit 102, scanning unit 104, and sensing unit 106 to actively adjust one or more aspects of the FOV of LIDAR system 100 to vary the orientation of the LIDAR FOV relative to the environment. In some cases, processing unit 108 may adjust one or more scan limits associated with scanning unit 104 to enlarge FOV 120. For example, one or more vertical tilt limits may be adjusted to increase a vertical dimension of the FOV, and/or one or more horizontal scan limits may be adjusted to increase a horizontal dimension of the FOV. In some cases, the FOV may be enlarged by increasing the scanning angle of the scanner (e.g., MEMS mirror, biaxial mechanical mirror, single axis scanners, etc.) along at least one axis/direction. In this example, and referring to Figs. 9B, 10B, and 11B, to account for a detected misalignment/misorientation of the LIDAR system relative to the environment, enlarging the LIDAR FOV may result in inclusion of regions 902, 1002, and 1102, respectively, in the enlarged LIDAR FOV.

[0163] As previously noted, however, while enlargement of a LIDAR FOV may be effective at compensating for a detected misalignment of a LIDAR system relative to an environment, such enlargement of the FOV may negatively impact certain operational aspects of the LIDAR system. For example, assuming no change in the scanning rate, a larger FOV may require more time to scan, which can result in an undesired reduction in the frame capture rate of a LIDAR system. Enlarging the LIDAR FOV can also result in scanning of certain regions (e.g., the sky, etc.) that may be of less interest or importance, as compared to other regions of the environment (e.g., just forward of a host vehicle, near a horizon, etc.). Enlarging a LIDAR FOV can result in inefficient application of scanning and computing resources.

[0164] In some embodiments, rather than increasing the size of the FOV, processing unit 108 may adjust one or more scan limits of scanning unit 104 to shift the location of the LIDAR FOV relative to the environment. Such a technique may ensure that a LIDAR FOV encompasses the desired regions of the environment while also avoiding inefficiencies associated with enlargement of the LIDAR FOV. As an example, referring to Fig. 9B, FOV 120 may be shifted downward by adjusting the vertical scan limits of scanning unit 104 to completely encompass region 902. As another example, in Fig. 10B, FOV 120 may be moved to left by adjust the horizontal scan limits of scanning unit 104 to encompass region 1002. And as yet another example, in Fig. 11B, FOV 120 may be rotated counterclockwise by adjusting together the horizontal and vertical scan limits of scanning unit 104 to encompass region 1102.

[0165] Fig. 13 is a flow chart representing an example method 1300 for correcting detected pitch misalignment of a LIDAR system relative to an environment. Consistent with the disclosed embodiments, method 1300 may be performed by LIDAR system 100 in Fig. 1 A. For example, in an embodiment, a LIDAR system for a host vehicle includes a laser emission unit configured to generate at least one laser beam, a scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system, and at least one processor (e.g., processing unit 108) programmed to perform one more or operations associated with detection and/or correction of a pitch misalignment between a LIDAR FOV and its environment.

[0166] In step 1302, processing unit 108 may define (e.g., within a captured LIDAR frame) a road surface plane indicative of a portion of a road surface in an environment of vehicle 110. For example, in some embodiments, processing unit 108 may identify a road surface plane in the environment of LIDAR system 100. The road surface plane may be indicative of a portion of a road surface in the environment of the host vehicle and may be defined based on point cloud information generated from the LIDAR system. For example, processing unit 108 may define the road surface plane with at least three points among the generated point cloud data determined to be located on a road surface in the environment of the host vehicle.

[0167] In step 1304, processing unit 108 may determine at least one indicator of a current pitch of LIDAR system 100 relative to the defined road surface plane. It should be noted that the pitch orientation of the LIDAR system also translates to the pitch orientation of the LIDAR system FOV. For example, as the LIDAR system pitches upward, so too will the orientation of the LIDAR system FOV relative to the environment. In some embodiments, the indicator of the current pitch of the LIDAR system may include a difference between a first direction associated with the road surface plane and a second direction associated with a reference plane. The first direction may be represented by a normal to the defined road surface plane, and the second direction may be represented by a normal to the reference plane.

[0168] In some examples, the reference plane corresponds to an expected location of the road surface plane relative to the LIDAR system in a case where the LIDAR system is properly aligned relative to the host vehicle and/or relative to its environment. For example, assuming a LIDAR system 100 is properly aligned with the host vehicle, and assuming the host vehicle resides on a flat road surface, for a properly aligned LIDAR system, the location of a reference plane (e.g., a plane represented by three or more points stored in memory) may coincide with the location of the road surface plane determined based on three or more pointcloud data points generated by the LIDAR system 100. Further, a line normal to the reference plane may be parallel (or nearly parallel) to a line normal to the determined road surface plane.

[0169] Misalignment of the LIDAR system relative to the host vehicle and/or relative to the environment, however, may result in discrepancies between the reference plane and the determined road surface plane. For example, if a LIDAR system is not properly oriented relative to the host vehicle or its environment, there may exist an angular offset between a line normal to the reference plane and a line normal to the determined road surface plane (which may also be expressed as an angular offset between the reference plane and the road surface plane, or any other suitable convention).

[0170] Fig. 14 conceptually represents a comparison between a reference plane 1402 and a determined road surface plane 1412. For example, road surface plane 1412 may be determined based on points D, E, and F included in point cloud data generated by the LIDAR system 100 (e.g., points corresponding to a detected road surface). Reference plane 1402 may be characterized by points A, B, and C and may represent the expected location of the road surface plane in a situation where the LIDAR system is properly aligned/oriented relative to the host vehicle and/or the environment. In this example, there is an angular offset between line 1404, which is normal to the reference plane 1402, and line 1414, which is normal to the road surface plane 1412. This angular offset may indicate that LIDAR system 100 is not properly aligned/oriented relative to the host vehicle and/or the environment. The magnitude of the angular offset (e.g., the size of the offset angle between lines 1404 and 1414) may indicate the degree of misalignment/misorientation of the LIDAR system relative to the host vehicle and/or the environment. The relative orientation of line 1414 with respect to line 1404 may also indicate a type of misorientation associated with the LIDAR system. For example, positive or negative angular offsets between lines 1404 and lines 1414 occurring in the Y-Z plane (assuming that reference plane 1402 coincides with the X-Y plane) may indicate that a current orientation of the LIDAR system includes a pitch misalignment. Positive or negative angular offsets between lines 1404 and lines 1414 occurring in the X-Z plane (again, assuming that reference plane 1402 coincides with the X-Y plane) may indicate that a current orientation of the LIDAR system includes a roll misalignment. And, combinations of the two may indicate that the LIDAR system is experiencing both pitch and roll misalignment.

[0171] Returning to Fig. 13, in step 1306, processing unit 108 may compare the current orientation of LIDAR system 100 (e.g., a pitch orientation represented by an angular difference in the Y-Z plane between lines 1404 and 1414) to a target orientation for LIDAR system 100 relative to the road surface plane (e.g., as indicated by line 1404 normal to the reference plane 1402). If a difference exists between the current orientation (e.g., pitch) of the LIDAR system and a target orientation (e.g., pitch) of the LIDAR system, then at step 1308 processing unit 108 may adjust at least one scan range limit associated with scanning unit 104 to at least partially compensate for a difference between the current orientation (e.g., pitch) of LIDAR system 100 and the target orientation (e.g., pitch) of LIDAR system 100. The scan range limit refers to any parameter that can be adjusted/changed to control the size of the LIDAR FOV. The scan limit may refer to the angular boundaries of a scanner (e.g., the rotational angle limits set for a scanner). Increasing the rotational angle limits of a scanner may result in an increase in size of the FOV (e.g., an increase in angular width or height of the FOV).

[0172] As an illustrative example, a particular scanner (e.g., a biaxial scanner) operating under normal conditions may have a horizontal scan range set at 90 degrees (+/- 45 degrees relative to an optical axis of the LIDAR system) and a vertical scan range set at 20 degrees (+/- 10 degrees relative to an optical axis of the LIDAR system). The scanner, however, may be capable of a wider scan range (e.g., 120 degrees in the horizontal direction and 40 degrees in the vertical direction). In the disclosed embodiments, this additional scanning range capability of the scanner may be used to rotate the LIDAR FOV relative to the environment. For example, if the LIDAR system pitches upward by 5 degrees, the FOV will also shift upward by 5 degrees. The optical axis of the LIDAR system will also pitch upward by 5 degrees. To return the FOV to an intended orientation relative to the environment, the vertical scan range of the scanner may be adjusted. For example, to counteract the 5-degree pitch upward, the scanner, which normally is set to scan vertically between +/- 10 degrees relative to an optical axis of the LIDAR system can be set to scan vertically from + 5 degrees and - 15 degrees relative to the optical axis of the LIDAR system. The size of the FOV will remain unchanged, and the scanner continues to operate within its available angular scan ranges.

[0173] The at least one scan range limit may be associated with at least one field of view limit of the LIDAR system. For example, in some examples, scanning unit 104 includes a biaxial scanner, and processing unit 108 may actuate scanning unit 104 to change a vertical tilt limit associated with the biaxial scanner. In some embodiments, scanning unit 104 includes at least one vertical scanner and at least one horizontal scanner, and processing unit 108 may actuate scanning unit 104 to change a vertical tilt limit associated with the at least one vertical scanner. Implementing similar directional changes to the upper and lower vertical tilt limits of either a biaxial scanner or a vertical scanning unit of a multi-scanner LIDAR system may shift the LIDAR FOV vertically relative to the environment of the LIDAR system.

[0174] In some embodiments, processing unit 108 may control scanning unit 104 to adjust the at least one scan range limit based on a magnitude of a determined difference between the current pitch of LIDAR system 100 and the target pitch for LIDAR system 100. For example, the amount of adjustment may be set to equal (and offset) the determined difference between the current pitch and the target pitch.

[0175] Differences between the current pitch of LIDAR system 100 and the target pitch for LIDAR system 110 can have various causes. For example, such a pitch misorientation may be due to weight distribution within vehicle 110. For example, the addition of heavy items to a trank or rear seat of a vehicle, or the addition of passengers to a rear seat of a vehicle, can result in pitch changes of the host vehicle and, in turn, of the LIDAR system. Such changes in pitch can be several degrees or more, which can result in significant displacement of a LIDAR FOV relative to the environment. Pitch changes may also be caused by variations in wind drag due to host vehicle velocity.

[0176] Changes in pitch of a LIDAR system relative to an environment can also be caused by changes in the environment. For example, a difference between a current pitch for a LIDAR system and a target pitch for the LIDAR system may result from a change in a road inclination relative to vehicle 110. For example, as a host vehicle approaches an incline, the road surface ahead of the host vehicle rises. As a result, the road surface plane detected based on points generated by the LIDAR system also rises and departs from the expected reference plane. In this situation, the difference in normal directions between the expected reference plane and the acquired road surface plane may indicate that the LIDAR FOV should be shifted relative to the environment. Specifically, the system may shift the LIDAR FOV upwards (by adjusting vertical scan limits of scanning unit 104, for example) such that the LIDAR FOV encompasses regions of the incline that may have been excluded from the LIDAR FOV prior to the shift.

[0177] Conversely, as a host vehicle approaches a decline, the road surface ahead of the host vehicle slopes downward. As a result, the road surface plane detected based on points generated by the LIDAR system tilts downward and departs from the expected reference plane. Again, the difference in normal directions between the expected reference plane and the acquired road surface plane may indicate that the LIDAR FOV should be shifted relative to the environment. Specifically, the system may shift the LIDAR FOV downwards (by adjusting vertical scan limits of scanning unit 104, for example) such that the LIDAR FOV encompasses regions of the decline that may have been excluded from the LIDAR FOV prior to the shift.

[0178] The road surface plane used for comparison to the expected reference plane may be generated based on any suitable segment of a road surface, and locations of the sampled points used to construct the road surface plane may be selected based on the requirements of a particular application. In some case, the road surface points may be selected randomly, and the selection may be based on a confidence level that the points are associated with the road. In some examples, the points selected for constructing the road surface plane may be closely located relative to the host vehicle. In some cases, a centroid of the selected points may reside within 20 meters of the host vehicle. In other cases, a centroid of the selected points may reside within 10 meters, 5 meters or 1 meter of the host vehicle. Constructing the road surface plane based on sampled points located near to the host vehicle may enable detection of a difference in orientation between the road surface plane and an expected reference plane. For example, generating the road surface plane based on point close to the host vehicle may decrease the likelihood that a difference in orientation between the road surface plane and the reference plane is due to environmental effects, such as an incline or decline along a road segment. That is, close in points may have a higher likelihood of residing in a similarly oriented plane as defined by the road surface immediately below the host vehicle. In such a situation, an observed difference in orientation between the road surface plane and the reference plane may be more likely caused by a change in orientation of the host vehicle relative to the road surface (e.g., pitch or roll due to weight distribution in the vehicle, wind drag, etc.) or by a change in orientation of the LIDAR system relative to the host vehicle (e.g., displacement due to impact, vibration, etc.). In this case, it may be less likely that the observed difference in orientation between the road surface plane and the reference plane is due to a nearby road segment incline or decline.

[0179] In other cases, the points selected for constructing the road surface plane may be more distantly located relative to the host vehicle. In some cases, a centroid of the selected points may be located at least 10 meters from the LIDAR system. In other cases, the centroid of the selected points may be located at least 20 meters from the LIDAR system, or at least 35 meters from the LIDAR system. Constructing the road surface plane based on sampled points located farther from the host vehicle may assist in detecting inclines or declines in a road segment. For example, in this situation, an observed difference in orientation between the road surface plane and the reference plane may indicate the presence of a road segment in the environment of the host vehicle that has an inclination different from a road surface on which the host vehicle currently resides.

[0180] In some cases, multiple sets of points from multiple different locations along a road segment can be used to construct the road surface plane. For example, a first road surface plane may be generated based on points closely located to a host vehicle, and a second road surface plane may be generated based on points more distantly located relative to the host vehicle. Each of the generated road surface planes can be compared to an expected road surface plane, and observed differences between the generated road surface planes and the expected road surface plane may be used to determine an appropriate response of the LIDAR system (e.g., whether to shift the LIDAR FOV relative to the environment, an amount by which to shift the LIDAR FOV, and a timing profile indicating when a shift to the LIDAR FOV should be implemented, etc.). A determination of whether, how, and when to shift a LIDAR FOV may be based on generation of any number of road surface planes and comparison of those road surface planes with an expected reference plane.

[0181] Moreover, a generated road surface plane may be based on points sampled from any suitable area of a road surface. In some cases, a road surface plane may be generated based on points representative of a road surface area of at least 5 square meters (or at least 10, 20, or 50 square meters). Increasing the road surface area represented by the sampled points may decrease the sensitivity of the LIDAR FOV alignment process to local variations in road surface inclination.

[0182] Other parameters may also be used to control the response sensitivity of the LIDAR FOV alignment system. For example, in some embodiments, a threshold difference between a generated road surface plane and an expected reference plane may be employed as a trigger for adjustment of the LIDAR FOV orientation relative to the environment, e.g., by adjusting at least one scan range limit. In some examples, processing unit 108 may initiate the adjustment of the LIDAR FOV orientation in response to an observation of at least a predetermined difference between the current pitch of LIDAR system 100 and the target pitch for LIDAR system 100. For example, the predetermined difference may be set at 0.05 degrees or greater. In other examples, the predetermined difference threshold may be set to the minimum pitch increment of the point cloud generated by LIDAR system 100.

[0183] One or more time delays may also be employed to control how the LIDAR FOV alignment system responds to an observed difference between a generated road surface plane and an expected reference plane. Such time delays may be useful for smoothing the responsiveness of the LIDAR FOV alignment system relative to changes in road surface inclination, for example. That is, to reduce the responsiveness of LIDAR system 100 to transient changes in LIDAR FOV orientation relative to an environment (whether caused by vibration, wind, changes in road surface inclination, etc.), processing unit 108 may initiate the adjustment of at least one scan range limit associated with scanning unit 104 after a predetermined time delay has elapsed. The longer the implemented time delay, the less responsive the system will be to short term variations in LIDAR FOV orientation relative to the environment.

[0184] Fig. 15A is a flow chart of a method 1500A, similar to method 1300 of Fig. 13 and includes steps 1502-1510. Compared to method 1300, method 1500A includes an additional step 1508A in which processing unit 108 determines whether a predetermined time delay has passed since detection of a difference (e.g., a difference exceeding a predetermined threshold) between a current orientation (e.g., pitch) of the LIDAR system and a target orientation (e.g., pitch) of the LIDAR system. If the predetermined time has elapsed, processing unit 108 proceeds to step 1510 to initiate the adjustment of the adjustment of the at least one scan range limit. The predetermined time delay may be selected according to the requirements of a particular application and/or according to a desired level of responsiveness for the LIDAR FOV alignment system. For example, the predetermined time delay may be between 0.2 seconds and 30 seconds. As another example, the predetermined time delay may be between 0.2 seconds and 10 seconds. As yet another example, the predetermined time delay may be between 0.2 seconds and 5 seconds.

[0185] As previously noted, in response to an observed difference between an actual and a target orientation for the LIDAR FOV relative to the environment, one or more scan limits associated with scanning unit 104 may be adjusted to cause a shift in orientation of the LIDAR FOV relative to the environment. Adjustment to the scan limits may occur instantaneously (e.g., the change(s) may be applied over the course of a minimum number of clock cycles required to implement the change — e.g., 1, 5, 10, 20 clock cycles). In some cases, however, the adjustment to the scan limits may be implemented over a predetermined period of time and at a predetermined rate of change. For example, processing unit 108 may be programmed to control scanning unit 104 to adjust at least one scan range limit to provide up to a +/-5 degree change in FOV of LIDAR system 100 (e.g., in orientation of the FOV relative to the environment), over a time period of between 1 second and 1 minute.

[0186] Other metrics may be used to control whether and how the LIDAR FOV alignment system responds to a detected difference between an actual FOV orientation and a target FOV orientation. For example, in some embodiments, initiation of an adjustment to at least one scan range limit associated with scanning unit 104 may be based on detected changes in the difference between the current pitch of LIDAR system 100 and the target pitch for LIDAR system 100, as shown in method 1500B of Fig. 15B. At step 1508B (Fig. 15B), processing unit 108 may determine whether a difference between the current orientation (e.g., pitch) of LIDAR system 100 and the target orientation (e.g., pitch) for LIDAR system 100 still exists after application of a predetermined time delay. If yes, processing unit 108 proceeds to step 1510 to initiate the adjustment to the at least one scan range limit. If not, processing unit 108 foregoes the adjustment and/or returns to step 1504 to start a new iteration. By such a process, the system may avoid responding to transient conditions that self-rectify within the predetermined time delay. The predetermined time delay may be between 0.2 seconds and 30 seconds. As another example, the predetermined time delay may be between 0.2 seconds and 10 seconds. As yet another example, the predetermined time delay may be between 0.2 seconds and 5 seconds.

[0187] In other cases, a detected difference in actual versus target FOV orientation may persist after a first predetermined time delay. If the difference is determined to be increasing, a change may be made to the LIDAR FOV orientation. On the other hand, if the difference is determined to be decreasing (meaning that the condition may be self-rectifying), a response to the detected difference may be delayed for a second predetermined time delay. For example, Fig. 15C shows a method 1500C in which processing unit 108 determines, after a predetermined time delay, whether the difference between the current orientation (e.g., pitch) of LIDAR system 100 and the target orientation (e.g., pitch) for LIDAR system 100 is trending toward zero (1508C1). If the difference is not trending toward zero after the first predetermined time delay, processing unit 108 proceeds to step 1510 to initiate the adjustment to the at least one scan range limit. If the difference is trending toward zero after the first predetermined time delay, processing unit 108 foregoes the adjustment for at least a second predetermined time delay (step 1508C2) and, after the second predetermined time delay has elapsed, returns to step 1504 to start a new iteration. The first and/or the second predetermined time delay may be between 0.2 seconds and 30 seconds. As another example, the first and/or the second predetermined time delay may be between 0.2 seconds and 10 seconds. As yet another example, the first and/or the second predetermined time delay may be between 0.2 seconds and 5 seconds.

[0188] As a further variation, Fig. 15D shows a method 1500D in which processing unit 108 determines, after a predetermined time delay, whether the difference between the current orientation (e.g., pitch) of LIDAR system 100 and the target orientation (e.g., pitch) for LIDAR system 100 is constant or increasing (step 1508D). If yes, processing unit 108 proceeds to step 1510 to initiate an adjustment to at least one scan range limit of scanning unit 104. If not, processing unit 108 foregoes the adjustment and/or returns to step 1504 to start a new iteration.

[0189] It should be noted that during application of a predetermined time delay (e.g., a first, second, or other time delay), the LIDAR system continues to scan the environment and generate corresponding point cloud representations of the environment. Therefore, during an applied time delay, at least one processor associated with the LIDAR system may determine whether a threshold difference between a generated road surface plane and an expected reference plane is increasing, has decreased, or has remained the same. Based on this information, by the time the predetermined time delay has elapsed, the processor may already have determined an appropriate course of action regarding whether to respond to the detected misalignment/misorientation.

[0190] In some cases, by observing multiple point clouds generated during a predetermined delay, the processor may determine a variation profile for the observed difference between a generated road surface plane and an expected reference plane (e.g., a time-dependent profile indicating how the observed difference has varied during the predetermined time delay). For example, the observed difference may oscillate between values above and below the predetermined threshold, or the observed difference periodically exceeds the threshold, but only for relative short bursts. In such cases, and based on observations of intervening point clouds generated during the predetermined time delay, a processor may determine to forego a response to the observed difference regardless of whether the difference continues to exist after the predetermined time delay has elapsed.

[0191] In some embodiments, the first and/or the second predetermined time delay may be automatically varied based on one or more detected conditions associated with an environment of the host vehicle. For example, when navigating in heavy traffic or urban environments, which may involve frequent vehicle pitch changes as a result of accelerations and decelerations, the first and/or the second predetermined time delays may be lengthened to avoid rapid or repeated adjustments of the LIDAR FOV orientation in response to transient conditions. On the other hand, while driving along an undulating roadway, the first and/or the second predetermined time periods may be reduced to increase the responsiveness of the FOV alignment system. In this way, the LIDAR FOV orientation may be shifted in response to detected road undulations such that the FOV encompasses regions of interest associated with the undulations.

[0192] Consistent with the disclosed embodiments, the above-described systems and methods for correcting pitch-related misalignments of a LIDAR FOV may also be used to correct roll-based misalignments of the LIDAR FOV relative to an environment. Fig. 16 is a flow chart of a method 1600 for compensating for a roll misalignment of a LIDAR FOV, without enlarging the FOV. Consistent with the disclosed embodiments, method 1600 may be performed by LIDAR system 100 in Fig. 1 A. For example, in an embodiment, a LIDAR system for a host vehicle includes a laser emission unit configured to generate at least one laser beam, a scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system, and at least one processor (e.g., processing unit 108) programmed to one more or operations, such as those shown in Fig. 16. As shown in Fig. 16, method 1600 includes at least the following steps 1602-1606.

[0193] In some embodiments, the scanning unit may include a biaxial scanning unit. Such a scanning unit may a MEMs biaxial scanner, a biaxial mechanically actuated mirror, etc. To change the size and or orientation of the LIDAR FOV, an optical control module used to control the scanner may issue a command to adjust a target scanning angle. If the target angle is larger than a maximum allowed angle, the adjustment may be made to the maximum allowed angle parameter. If the adjustment rate required is too high to implement in a single adjustment, several smaller adjustments may be made over a longer period to enable an angular speed below a threshold angular speed.

[0194] In step 1602, processing unit 108 may detect a current roll orientation of the LIDAR system 100. In some embodiments, similar to the above-described steps 1302 and 1304 (Fig. 13), processing unit 108 may define, within a captured LIDAR frame, a road surface plane indicative of a portion of a road surface in an environment of the vehicle 110, and may determine at least one indicator of a current roll orientation of the LIDAR system 100 relative to the defined road surface plane. In some embodiments, the indicator of the current roll orientation may include a difference between the determined road surface plane and an expected reference plane. The difference may be determined based on an angular difference between a normal to the determined road surface plane and a normal to the expected reference plane. The reference plane may correspond to an expected location of the road surface plane in a captured LIDAR frame under conditions of correct roll orientation of the LIDAR FOV relative to an environment.

[0195] In step 1604, processing unit 108 may determine a difference between the current roll orientation and a target roll orientation for the LIDAR system 100. In some embodiments, similar to the above-described step 1306 (Fig. 13), processing unit 108 may compare the current roll orientation for LIDAR system 100 to a target roll orientation for LIDAR system 100. In one embodiment, processing unit 108 may compare the current roll orientation to the target roll orientation, based on the difference between the normal direction associated with the actual road surface plane and the normal direction associated with the expected road surface plane (determined in step 1302).

[0196] In step 1606, processing unit 108 may adjust a roll orientation of the scanning unit 104 to at least partially compensate for the difference between the current roll orientation and a target roll orientation for LIDAR system 100. In some embodiments, the scanning unit 104 in the LIDAR system 100 may be a biaxial scanning unit configured to project at least one laser beam toward the FOV of LIDAR system 100. As in the processes described above for correcting discrepancies in pitch orientation of the LIDAR FOV relative to the environment, in response to detection of a roll misalignment, the processing unit 108 may adjust scan limits associated with scanning unit 104. Here, however, processing unit 108 may adjust a vertical tilt angle of a biaxial scanning unit while also adjusting a horizontal scan angle of the biaxial scanning unit to at least partially compensate for the difference between the current roll orientation and the target roll orientation for LIDAR system 100. Changing the horizontal scan limits while also changing the vertical scan limits may enable a counterclockwise or clockwise rotation of the LIDAR FOV relative to the environment (e.g., by scanning along diagonally oriented scan lines).

Example Implementation: LIDAR System with Active Yaw Control

[0197] In addition to pitch and roll related alignment of a LIDAR FOV relative to an environment, adjustments to a LIDAR FOV may also be made in a yaw direction (see Fig. 7). Such adjustments may be made in response to yaw misalignment between LIDAR system 100 and a host vehicle (e.g., misalignments introduced during manufacturing or as a result of damage to the LIDAR system or host vehicle, misalignments due to changes in wind direction, vibration, etc.). Such adjustments may also be made in response to conditions experienced by the host vehicle and LIDAR system. For example, as a host vehicle approaches a curve in a roadway, there may be an interest in biasing a yaw orientation of the LIDAR FOV in the direction of the curve such that objects residing in the curve may be detected more readily and/or sooner than if the LIDAR FOV remained non-biased. Similarly, depending on the location of a host vehicle along a roadway, there may be an interest in biasing the LIDAR FOV in one yaw direction or another. For example, if a host vehicle travels on a three-lane highway and is located in the left-most lane, there may be an interest in biasing the yaw orientation of the LIDAR FOV to the right so that objects in the middle and right lanes may be more readily detected. Similarly, if a host vehicle travels on a right-most lane of a three-lane highway, there may be an interest in biasing the yaw orientation of the LIDAR FOV to the left so that objects in the middle and right lanes may be more readily detected. [0198] Fig. 17 is a flow chart of a method 1700 for adjusting a LIDAR system’s yaw alignment relative to an environment. Consistent with the disclosed embodiments, method 1700 may be performed by LIDAR system 100 in Fig. 1 A. For example, in an embodiment, a LIDAR system for a host vehicle includes a laser emission unit configured to generate at least one laser beam, a scanning unit configured to project the at least one laser beam toward a field of view of the LIDAR system, and at least one processor (e.g., processing unit 108) programmed to perform one or more operations.

[0199] For example, at step 1702, processing unit 108 may determine at least one indicator of a current yaw orientation of LIDAR system 100 based on analysis of point cloud representations of at least one stationary object in an environment of vehicle 110 and based on detected ego motion of vehicle 110. “Ego motion” refers to the motion profile (e.g., speed, path, etc.) of a host vehicle as it travels through the environment. Aspects of a host vehicle’s ego motion may be detected based on outputs from one or more sensors associated with the host vehicle. Such sensors may include, among others, one or more speed sensors, an odometer, a GPS unit, an accelerometer, an inertial motion sensor, etc. Consistent with the disclosed embodiments, the ego motion of vehicle 110 may be detected and tracked using any suitable technique. In one example, a host vehicle’ s ego motion may be determined using a simultaneous localization and mapping (SLAM) technique, which uses available sensor data to construct a map of the environment of the host vehicle while tracking a position of the host vehicle relative to the constructed map.

[0200] The yaw orientation of a LIDAR FOV relative to an environment may be determined using various techniques. In one example, processing unit 108 may determine at least one indicator of the current yaw orientation of LIDAR system 100 by first identifying at least one stationary object represented in a sequence of generated point clouds. The processing unit 108 may then analyze a trajectory of the at least one stationary object over the sequence of generated point clouds, while accounting for the effects of detected vehicle ego motion. For example, ego motion of a host vehicle through an environment will cause apparent motion of detected objects in the environment across a series of point clouds generated based on LIDAR scans of the environment. The motion trajectories of the detected objects across the generated point clouds will depend on the ego motion of the host vehicle. In one example, if a host vehicle is approaching a traffic sign located on an edge of a road to the right of the host vehicle, then in a series of point clouds generated by a forward-facing LIDAR on the host vehicle, the representation of the traffic sign in the series of point clouds will grow in size and appear to follow a trajectory down and to the right across the series of point clouds. Similarly, as the host vehicle approaches a traffic sign on the left edge of the road, the representation of the traffic sign in the series of point clouds will grow in size and appear to follow a trajectory down and to the left across the series of point clouds.

[0201] This effect may be used to determine a yaw orientation of a LIDAR FOV relative to the environment. For example, in the scenario described above, if the left and right traffic signs are disposed on a line normal to a direction of travel of the host vehicle, and if the host vehicle direction of travel bisects that line, then the expectation for a perfectly forwardfacing LIDAR (e.g., 0 yaw angle) would be that representations of the left and right traffic signs in the generated point clouds would follow trajectories that mirror one another. The left traffic sign representation would appear to travel down and to the left, and the right traffic sign representation would appear to travel down and to the right by equal and opposite amounts. If the LIDAR system is positioned relative to the host vehicle with a non-zero yaw angle, however, the left and right traffic signs will no longer follow trajectories that mirror one another. For example, if the LIDAR system is biased to the right (positive yaw angle), then the representation of the right traffic sign may still appear to follow a trajectory down and to the right, but the origin of the traffic sign representation in the series of point clouds may begin farther to the left in the point clouds, may not travel as far on the Y axis in the point clouds, and may travel farther to the right along the X axis in the point clouds. And, where the LIDAR system is biased to the right, the representation of the left traffic sign may appear to follow a trajectory across the series of point clouds that begins at an origin farther to the left and that may extend downward, but less to the left (and even to the right in some cases).

[0202] Based on these principles, the observed trajectory of a stationary object representation through a series of generated point clouds (e.g., two or more) may be used in conjunction with the vehicle ego motion to determine the actual (or current) yaw orientation of a LIDAR system relative to the environment. For example, if the ego motion of the host vehicle is known, then the expected trajectory of a particular stationary object representation through a series of generated point clouds (assuming a LIDAR system oriented with a yaw angle of zero degrees relative to host vehicle direction of travel) can be determined. This is one way that vehicle ego motion can be accounted for in the analysis of object trajectories across a plurality of generated point clouds. The expected trajectory can be compared to an actual observed trajectory, and based on an observed difference, the yaw orientation (e.g., yaw angle) of the LIDAR system relative to the host vehicle travel direction (or the orientation of the LIDAR FOV relative to the environment) can be determined. In this example, analysis of a trajectory of the at least one stationary object over a sequence of generated point clouds, while accounting for the effects of detected vehicle ego motion refers to tracking the actual observed trajectory of the object across the point clouds and comparing the actual observed trajectory to the expected vehicle ego motion-induced trajectory where the LIDAR system has an expected yaw alignment relative to the vehicle. A difference between the actual observed trajectory and the expected, ego motion-based trajectory indicates a yaw alignment of the LIDAR system different from an expected yaw alignment. The characteristics of observed differences between the expected trajectory and the actual observed trajectory (e.g,, the directional variation of the actual trajectory versus the expected trajectory and the magnitude of observed differences between the actual and expected trajectories) may indicate the direction and degree of LIDAR system yaw alignment away from an expected alignment.

[0203] Returning to Fig. 17, in step 1704, processing unit 108 may determine a difference between the current yaw orientation and a target yaw orientation for LIDAR system 100. Consistent with the disclosed embodiments, the target yaw angle may be a reference yaw angle that is predetermined (e.g., set at certain predetermined angular value (0 degrees, +/- 5 degrees, +/- 10 degrees, +/- 30 degrees, +/- 45 degrees, +/- 90 degrees, etc.) relative to the longitudinal axis of vehicle 110). In other cases, the target yaw angle may be dynamically determined during operation of the host vehicle and based on observed conditions (e.g., a lane location/lane assignment of the host vehicle, an approaching curved road segment, etc.).

[0204] In one example, the target yaw orientation (i.e., desired yaw orientation) for LIDAR system 100 may vary based on sensed driving conditions. In some embodiments, processing unit 108 may determine the target yaw orientation based on heading direction of the host vehicle 110. For example, if vehicle 110 is turning to the right, processing unit 108 may bias the target yaw orientation toward a right side of vehicle 110. If vehicle 110 is turning to the left, processing unit 108 may bias the target yaw orientation toward a left side of vehicle 110. Changes in the target yaw orientation may also be made based on upcoming road features. For example, if a leftward turning road segment is detected ahead of the host vehicle (e.g., based on analysis of generated point clouds, acquired images, etc.), processing unit 108 may bias the target yaw orientation toward a left side of vehicle 110. Similarly, if a rightward turning road segment is detected ahead of the host vehicle, processing unit 108 may bias the target yaw orientation toward a right side of vehicle 110. The bias may be implemented prior to the host vehicle arriving at the curved road segment, as the host vehicle enters the curved road segment, and/or the bias may be progressively varied as the host vehicle enters a curved road segment and travels along the curved road segment. As yet another example, if vehicle 110 is detected as moving in a straight direction, processing unit 108 may establish a non-biased target yaw orientation (e.g., setting the target yaw orientation to be along the longitudinal axis of vehicle 110).

[0205] In some embodiments, processing unit 108 may determine the target yaw orientation of the LIDAR system based on a lateral position of vehicle 110 on a road segment. For example, if vehicle 110 has a lateral position toward a right edge of the road segment, processing unit 108 may bias the target yaw orientation toward a left side of vehicle 110 (e.g., setting the target yaw orientation to be 0.5 to ten degrees to the left of the longitudinal axis of vehicle 110). Biasing the yaw orientation of the LIDAR system in this manner may offer improved detection, monitoring, etc. capabilities of target vehicles traveling along the same road as the host vehicle (e.g., because the LIDAR system yaw orientation is biased toward locations where most of the target vehicles are expected to be located). Similarly, if a lateral position of vehicle 110 is toward a left edge of the road segment, processing unit 108 may bias the target yaw orientation of the LIDAR system toward a right side of vehicle 110 (e.g., setting the target yaw orientation to be 0.5 to 10 degrees to the right of the longitudinal axis of vehicle 110).

[0206] In some embodiments, processing unit 108 may determine the target yaw orientation for the LIDAR system based on a lane location or lane assignment of vehicle 110 on a road segment. For example, if vehicle 110 is located in a right lane location on the road segment, processing unit 108 may bias the target yaw orientation toward a lane to the left side of vehicle 110. As another example, if vehicle 110 is travelling in a left lane location on the road segment, processing unit 108 may bias the target yaw orientation toward a lane to the right side of the host vehicle. As yet another example, if vehicle 110 is travelling in a middle lane location on the road segment, processing unit 108 may determine a non-biased target yaw orientation (e.g., setting the target yaw orientation to be along the longitudinal axis of vehicle 110). Actively controlling the target yaw orientation for the LIDAR system in this manner may assist in gathering information relevant to a certain navigational situation. For example, when a vehicle is traveling in a right lane, there may be an interest in enhanced detection capability of objects in leftward lanes, which may be involved in a future lane change maneuver, etc.

[0207] In step 1706, processing unit 108 may adjust at least one scan range limit associated with scanning unit 104 to at least partially compensate for a difference between the current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100. For example, to bias the LIDAR FOV to the left, the horizontal scan limit associated with the left side of the FOV may be increased. Additionally, the horizontal scan limit associated with the right side of the FOV may be decreased. Conversely, to bias the LIDAR FOV to the right, the horizontal scan limit associated with the right side of the FOV may be increased. Additionally, the horizontal scan limit associated with the left side of the FOV may be decreased.

[0208] In another example, the adjustment of the at least one scan range limit associated with scanning unit 104 may involve changing a horizontal scan range limit of scanning unit 104. In some embodiments, scanning unit 104 may include a biaxial scanner, and processing unit 108 may actuate scanning unit 104 to change at least one horizontal scan limit of the biaxial scanner. This results a horizontal shift of the LIDAR FOV relative to the environment. In some embodiments, scanning unit 104 may include at least one vertical scanner and at least one horizontal scanner, and processing unit 108 may cause a shift in the relative orientation of the LIDAR FOV by causing a change to at least one horizontal scan range limit associated with the at least one horizontal scanner of scanning unit 104. Such a change may also cause a horizontal shift of LIDAR system 100’s FOV.

[0209] In some embodiments, an amount of an adjustment made to a horizontal scan limit associated with scanning unit 104 may be dependent upon a magnitude of the difference between the current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100. For example, processing unit 108 may cause the horizontal scan range limit of scanning unit 104 to shift by a yaw angle equal to a magnitude of a difference between the current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100.

[0210] In some embodiments, an adjustment to a horizontal scan limit to shift a relative orientation of the LIDAR FOV may be made upon detection of a difference between a current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100. In other cases, the adjustment may be implemented after a predetermined time delay. As an example, the predetermined time delay may be between 0.2 seconds and 30 seconds. As another example, the predetermined time delay may be between 0.2 seconds and 10 seconds. As yet another example, the predetermined time delay may be between 0.2 seconds and 1 second.

[0211] Adjustment of the yaw orientation of LIDAR system 100 may also be based on other factors. For example, in some embodiments, the initiation of an adjustment to at least one scan range limit of the scanning unit 104 may be based on detected changes in the difference between the current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100. In some cases, processing unit 108 may determine whether a detected difference between the current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100 persists after a predetermined time delay. If yes, processing unit 108 proceeds to step 1706 to initiate the adjustment to the at least one scan range limit. If not, processing unit 108 foregoes the adjustment and/or returns to step 1702 to start a new iteration of method 1700.

[0212] In another example, processing unit 108 may determine, after a first predetermined time delay, whether a detected difference between the current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100 is trending toward zero. If the difference is not trending toward zero after the first predetermined time delay, processing unit 108 proceeds to step 1706 to initiate the adjustment to the at least one scan range limit. If, after the first predetermined time delay, the difference is trending toward zero, processing unit 108 may forego the adjustment for at least a second predetermined time delay to provide additional time for the difference to return to zero. After the second predetermined time delay has elapsed, the process may return to step 1702 to start a new iteration of method 1700.

[0213] The first and/or second predetermined time delays may be between 0.2 seconds and 30 seconds. In other cases, the first and/or second predetermined time delays may be between 0.2 seconds and 10 seconds, or between 0.2 seconds and 1 second.

[0214] In another exemplary embodiment, processing unit 108 may determine whether a detected difference between the current yaw orientation of LIDAR system 100 and the target yaw orientation for LIDAR system 100 is constant or increasing after a predetermined time delay. If yes, processing unit 108 proceeds to step 1706 to initiate the adjustment to the at least one scan range limit. If not, processing unit 108 foregoes the adjustment and/or returns to step 1702 to start a new iteration of method 1700.

[0215] The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the art will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, or other optical drive media.

[0216] Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. The various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/ AJAX combinations, XML, or HTML with included Java applets.

[0217] Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that the specification and examples be considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.