CROSS-REFERENCE TO RELATED APPLICATIONS

[1] This application claims the priority benefit of the US Provisional Patent Application 63/410,641 filed on September 28, 2022, entitled DYNAMIC ANTENNA SYSTEM, and is related to US Provisional Patent Application No. 63/409,236 filed on September 23, 2022, entitled “HIGH DENSITY ANTENNA SYSTEM”. The entire content of each application referenced in this paragraph is hereby incorporated by reference herein and made part of this specification.

BRIEF DESCRIPTION OF THE FIGURES

[2] FIG. 1 is an example environment in which a vehicle including one or more components of an autonomous system can be implemented.

[3] FIG. 2 is a diagram of one or more example systems of a vehicle including an autonomous system.

[4] FIG. 3 is a diagram of components of one or more example devices and/or one or more example systems of FIGS. 1 and 2.

[5] FIG. 4A is a diagram of certain components of an example autonomous system.

[6] FIG. 4B is a diagram of an example implementation of a neural network.

[7] FIG. 4C and 4D are a diagram illustrating example operation of a CNN;

[8] FIGS. 5A-5C are diagrams of a non-dynamic antenna system.

[9] FIG. 6 is a diagram of an example implementation of a dynamic antenna system.

[10] FIGS. 7A-7B are diagrams of an example implementation of a dynamic antenna system.

[11] FIGS. 8A-8B are diagrams of an example implementation of a dynamic antenna system.

[12] FIGS. 9A-9B are diagrams of antenna system.

[13] FIG. 10 is a diagram of an example implementation of a dynamic antenna system. [14] FIG. 1 1 is a diagram of an example implementation of a dynamic antenna system associated with a machine learning model.

[15] FIG. 12 is a diagram of an example implementation of a dynamic antenna system associated with a machine learning model.

[16] FIG. 13 is a flowchart of an example process for a dynamic antenna system.

DETAILED DESCRIPTION

[17] Autonomous driving, such as for autonomous vehicles, requires a large amount of wireless connectivity, such as V2X and LTE, in order to operate in an environment. Wireless connectivity can be used for non-autonomous driving as well.

[18] V2X is “vehicle to everything” communication. V2X includes communication between a vehicle and an entity such as infrastructure (e.g., traffic lights), another vehicle, a pedestrian, and/or other autonomous vehicles. V2X antennas should also be on a roof to give 360-degree coverage around vehicle.

[19] Long-Term Evolution (LTE) is cellular communication, such as for fleet management, Over-The-Air (OTA) software updates, remote vehicle assistance, and other mission services.

[20] All of these types of communications and wireless connectivity require antennas, which may be challenging to robustly implement in a vehicle.

[21] In the following description numerous specific details are set forth in order to provide a thorough understanding of the present disclosure for the purposes of explanation. It will be apparent, however, that the embodiments described by the present disclosure can be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form in order to avoid unnecessarily obscuring aspects of the present disclosure.

[22] Specific arrangements or orderings of schematic elements, such as those representing systems, devices, modules, instruction blocks, data elements, and/or the like are illustrated in the drawings for ease of description. However, it will be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required unless explicitly described as such. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments unless explicitly described as such.

[23] Further, where connecting elements such as solid or dashed lines or arrows are used in the drawings to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the disclosure. In addition, for ease of illustration, a single connecting element can be used to represent multiple connections, relationships or associations between elements. For example, where a connecting element represents communication of signals, data, or instructions (e.g., “software instructions”), it should be understood by those skilled in the art that such element can represent one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication.

[24] Although the terms first, second, third, and/or the like are used to describe various elements, these elements should not be limited by these terms. The terms first, second, third, and/or the like are used only to distinguish one element from another. For example, a first contact could be termed a second contact and, similarly, a second contact could be termed a first contact without departing from the scope of the described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.

[25] The terminology used in the description of the various described embodiments herein is included for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well and can be used interchangeably with “one or more” or “at least one,” unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this description specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[26] As used herein, the terms “communication” and “communicate” refer to at least one of the reception, receipt, transmission, transfer, provision, and/or the like of information (or information represented by, for example, data, signals, messages, instructions, commands, and/or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and/or the like) to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and/or send (e.g., transmit) information to the other unit. This may refer to a direct or indirect connection that is wired and/or wireless in nature. Additionally, two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and/or routed between the first and second unit. For example, a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second unit if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes information received from the first unit and transmits the processed information to the second unit. In some embodiments, a message may refer to a network packet (e.g., a data packet and/or the like) that includes data.

[27] As used herein, the term “if” is, optionally, construed to mean “when”, “upon”, “in response to determining,” “in response to detecting,” and/or the like, depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining,” “in response to determining,” “upon detecting [the stated condition or event],” “in response to detecting [the stated condition or event],” and/or the like, depending on the context. Also, as used herein, the terms “has”, “have”, “having”, or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

[28] “At least one," and "one or more" includes a function being performed by one element, a function being performed by more than one element, e.g., in a distributed fashion, several functions being performed by one element, several functions being performed by several elements, or any combination of the above.”

[29] Some embodiments of the present disclosure are described herein in connection with a threshold. As described herein, satisfying, such as meeting, a threshold can refer to a value being greater than the threshold, more than the threshold, higher than the threshold, greater than or equal to the threshold, less than the threshold, fewer than the threshold, lower than the threshold, less than or equal to the threshold, equal to the threshold, and/or the like.

[30] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments can be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

General Overview

[31] In some aspects and/or embodiments, systems, methods, a vehicles described herein for a dynamic antenna system. The antenna system can be for an autonomous vehicle. The antenna system includes at least one modem. The antenna system includes at least one antenna operatively connected with the at least one modem. The antenna system is configured to determine, using at least one processor, a performance parameter indicative of a performance of a communication between the at least one antenna and a network node. In some embodiments, the antenna system is configured to control, using the at least one processor, based on the performance parameter, one or more of a position of the at least one antenna, and a connection of the at least one antenna to the at least one modem. In one or more examples or embodiments, the antenna system includes at least one processor configured to execute instructions associated with a machine learning model. In some such examples, the machine learning model is trained using the antenna system during a training period. In some cases, the machine learning model can be trained using one or more of the same antenna systems mounted on one or more vehicles moving in an area. In these cases, the machine learning model can be trained to control a position of an antenna of the antenna system moving in the same area based on the performance parameter such that, e.g., the strength and/or signal-to-noise-ratios of wireless signals received from one or more network nodes satisfies (e.g., is above) a threshold value.

[32] By virtue of the implementation of systems, methods, and computer program products described herein, techniques for operation of a dynamic antenna system are disclosed. Some of the advantages of these techniques include improving connections between an autonomous vehicle and a network node, which can allow for improved performance, such as improved communication, and avoid disconnections between an autonomous vehicle and a network. Further advantages include increased flexibility and maximization of connection availability. The techniques can reduce the risk of an autonomous vehicle being unreachable due to poor or limited signal coverage. Moreover, the training, inclusion, and use of machine learning models can reduce overall power consumption as a full scan may not be needed for determining performance.

[33] Referring now to FIG. 1 , illustrated is example environment 100 in which vehicles that include autonomous systems, as well as vehicles that do not, are operated. As illustrated, environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 1 12, remote autonomous vehicle (AV) system 1 14, fleet management system 116, and V2I system 1 18. Vehicles 102a-102n, vehicle-to-infrastructure (V2I) device 110, network 1 12, autonomous vehicle (AV) system 1 14, fleet management system 116, and V2I system 1 18 interconnect (e.g., establish a connection to communicate and/or the like) via wired connections, wireless connections, or a combination of wired or wireless connections. In some embodiments, objects 104a-104n interconnect with at least one of vehicles 102a-102n, vehicle-to-infrastructure (V2I) device 110, network 1 12, autonomous vehicle (AV) system 114, fleet management system 1 16, and V2I system 1 18 via wired connections, wireless connections, or a combination of wired or wireless connections.

[34] Vehicles 102a-102n (generically referred to as vehicle(s) 102) include at least one device configured to transport goods and/or people. In some embodiments, vehicles 102 are configured to be in communication with V2I device 110, remote AV system 1 14, fleet management system 1 16, and/or V2I system 118 via network 1 12. In some embodiments, vehicles 102 include cars, buses, trucks, trains, and/or the like. In some embodiments, vehicles 102 are the same as, or similar to, vehicles 200, described herein (see FIG. 2). In some embodiments, a vehicle 200 of a set of vehicles 200 is associated with an autonomous fleet manager. In some embodiments, vehicles 102 travel along respective routes 106a-106n (generically referred to as route(s) 106), as described herein. In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[35] Objects 104a-104n (generically referred to as object(s) 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, at least one structure (e.g., a building, a sign, a fire hydrant, etc.), and/or the like. Each object 104 is stationary (e.g., located at a fixed location for a period of time) or mobile (e.g., having a velocity and associated with at least one trajectory). In some embodiments, objects 104 are associated with corresponding locations in area 108.

[36] Routes 106a-106n (generically referred to as route(s) 106) are each associated with (e.g., prescribe) a sequence of actions (also known as a trajectory) connecting states along which an AV can navigate. Each route 106 starts at an initial state (e.g., a state that corresponds to a first spatiotemporal location, velocity, and/or the like) and ends at a final goal state (e.g., a state that corresponds to a second spatiotemporal location that is different from the first spatiotemporal location) or goal region (e.g. a subspace of acceptable states (e.g., terminal states)). In some embodiments, the first state includes a location at which an individual or individuals are to be picked-up by the AV and the second state or region includes a location or locations at which the individual or individuals picked-up by the AV are to be dropped-off. In some embodiments, routes 106 include a plurality of acceptable state sequences (e.g., a plurality of spatiotemporal location sequences), the plurality of state sequences associated with (e.g., defining) a plurality of trajectories. In an example, routes 106 include only high level actions or imprecise state locations, such as a series of connected roads dictating turning directions at roadway intersections. Additionally, or alternatively, routes 106 may include more precise actions or states such as, for example, specific target lanes or precise locations within the lane areas and targeted speed at those positions. In an example, routes 106 include a plurality of precise state sequences along the at least one high level action sequence with a limited lookahead horizon to reach intermediate goals, where the combination of successive iterations of limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form the high level route to terminate at the final goal state or region.

[37] Area 108 includes a physical area (e.g., a geographic region) within which vehicles 102 can navigate. In an example, area 108 includes at least one state (e.g., a country, a province, an individual state of a plurality of states included in a country, etc.), at least one portion of a state, at least one city, at least one portion of a city, etc. In some embodiments, area 108 includes at least one named thoroughfare (referred to herein as a “road”) such as a highway, an interstate highway, a parkway, a city street, etc. Additionally, or alternatively, in some examples area 108 includes at least one unnamed road such as a driveway, a section of a parking lot, a section of a vacant and/or undeveloped lot, a dirt path, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that can be traversed by vehicles 102). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking.

[38] Vehicle-to-lnfrastructure (V2I) device 1 10 (sometimes referred to as a Vehicle-to- Infrastructure or Vehicle-to-Everything (V2X) device) includes at least one device configured to be in communication with vehicles 102 and/or V2I infrastructure system 1 18. In some embodiments, V2I device 1 10 is configured to be in communication with vehicles 102, remote AV system 1 14, fleet management system 1 16, and/or V2I system 1 18 via network 1 12. In some embodiments, V2I device 110 includes a radio frequency identification (RFID) device, signage, cameras (e.g., two-dimensional (2D) and/or three- dimensional (3D) cameras), lane markers, streetlights, parking meters, etc. In some embodiments, V2I device 1 10 is configured to communicate directly with vehicles 102. Additionally, or alternatively, in some embodiments V2I device 110 is configured to communicate with vehicles 102, remote AV system 1 14, and/or fleet management system 116 via V2I system 1 18. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.

[39] Network 1 12 includes one or more wired and/or wireless networks. In an example, network 112 includes a cellular network (e.g., a long term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, etc., a combination of some or all of these networks, and/or the like.

[40] Remote AV system 114 includes at least one device configured to be in communication with vehicles 102, V2I device 110, network 112, fleet management system 116, and/or V2I system 118 via network 112. In an example, remote AV system 1 14 includes a server, a group of servers, and/or other like devices. In some embodiments, remote AV system 1 14 is co-located with the fleet management system 1 16. In some embodiments, remote AV system 114 is involved in the installation of some or all of the components of a vehicle, including an autonomous system, an autonomous vehicle compute, software implemented by an autonomous vehicle compute, and/or the like. In some embodiments, remote AV system 114 maintains (e.g., updates and/or replaces) such components and/or software during the lifetime of the vehicle.

[41] Fleet management system 1 16 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 1 14, and/or V2I infrastructure system 118. In an example, fleet management system 1 16 includes a server, a group of servers, and/or other like devices. In some embodiments, fleet management system 116 is associated with a ridesharing company (e.g., an organization that controls operation of multiple vehicles (e.g., vehicles that include autonomous systems and/or vehicles that do not include autonomous systems) and/or the like).

[42] In some embodiments, V2I system 118 includes at least one device configured to be in communication with vehicles 102, V2I device 1 10, remote AV system 114, and/or fleet management system 116 via network 1 12. In some examples, V2I system 118 is configured to be in communication with V2I device 1 10 via a connection different from network 112. In some embodiments, V2I system 118 includes a server, a group of servers, and/or other like devices. In some embodiments, V2I system 1 18 is associated with a municipality or a private institution (e.g., a private institution that maintains V2I device 1 10 and/or the like).

[43] In some embodiments, device 300 is configured to execute software instructions of one or more steps of the disclosed method, as illustrated in FIG. 13.

[44] The number and arrangement of elements illustrated in FIG. 1 are provided as an example. There can be additional elements, fewer elements, different elements, and/or differently arranged elements, than those illustrated in FIG. 1. Additionally, or alternatively, at least one element of environment 100 can perform one or more functions described as being performed by at least one different element of FIG. 1. Additionally, or alternatively, at least one set of elements of environment 100 can perform one or more functions described as being performed by at least one different set of elements of environment 100.

[45] Referring now to FIG. 2, vehicle 200 (which may be the same as, or similar to vehicle 102 of FIG. 1 ) includes or is associated with autonomous system 202, powertrain control system 204, steering control system 206, and brake system 208. In some embodiments, vehicle 200 is the same as or similar to vehicle 102 (see FIG. 1 ). In some embodiments, autonomous system 202 is configured to confer vehicle 200 autonomous driving capability (e.g., implement at least one driving automation or maneuver-based function, feature, device, and/or the like that enable vehicle 200 to be partially or fully operated without human intervention including, without limitation, fully autonomous vehicles (e.g., vehicles that forego reliance on human intervention such as Level 5 ADS-operated vehicles), highly autonomous vehicles (e.g., vehicles that forego reliance on human intervention in certain situations such as Level 4 ADS-operated vehicles), conditional autonomous vehicles (e.g., vehicles that forego reliance on human intervention in limited situations such as Level 3 ADS-operated vehicles) and/or the like. In one embodiment, autonomous system 202 includes operational or tactical functionality required to operate vehicle 200 in on-road traffic and perform part or all of Dynamic Driving Task (DDT) on a sustained basis. In another embodiment, autonomous system 202 includes an Advanced Driver Assistance System (ADAS) that includes driver support features. Autonomous system 202 supports various levels of driving automation, ranging from no driving automation (e.g., Level 0) to full driving automation (e.g., Level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may be made to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, which is incorporated by reference in its entirety. In some embodiments, vehicle 200 is associated with an autonomous fleet manager and/or a ridesharing company.

[46] Autonomous system 202 includes a sensor suite that includes one or more devices such as cameras 202a, LiDAR sensors 202b, radar sensors 202c, and microphones 202d. In some embodiments, autonomous system 202 can include more or fewer devices and/or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), odometry sensors that generate data associated with an indication of a distance that vehicle 200 has traveled, and/or the like). In some embodiments, autonomous system 202 uses the one or more devices included in autonomous system 202 to generate data associated with environment 100, described herein. The data generated by the one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located. In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle compute 202f, drive-by-wire (DBW) system 202h, and safety controller 202g.

[47] Cameras 202a include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Cameras 202a include at least one camera (e.g., a digital camera using a light sensor such as a Charge-Coupled Device (CCD), a thermal camera, an infrared (IR) camera, an event camera, and/or the like) to capture images including physical objects (e.g., cars, buses, curbs, people, and/or the like). In some embodiments, camera 202a generates camera data as output. In some examples, camera 202a generates camera data that includes image data associated with an image. In this example, the image data may specify at least one parameter (e.g., image characteristics such as exposure, brightness, etc., an image timestamp, and/or the like) corresponding to the image. In such an example, the image may be in a format (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments, camera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision). In some examples, camera 202a includes a plurality of cameras that generate image data and transmit the image data to autonomous vehicle compute 202f and/or a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 1 16 of FIG. 1 ). In such an example, autonomous vehicle compute 202f determines depth to one or more objects in a field of view of at least two cameras of the plurality of cameras based on the image data from the at least two cameras. In some embodiments, cameras 202a is configured to capture images of objects within a distance from cameras 202a (e.g., up to 100 meters, up to a kilometer, and/or the like). Accordingly, cameras 202a include features such as sensors and lenses that are optimized for perceiving objects that are at one or more distances from cameras 202a.

[48] In an embodiment, camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs and/or other physical objects that provide visual navigation information. In some embodiments, camera 202a generates traffic light data associated with one or more images. In some examples, camera 202a generates TLD (Traffic Light Detection) data associated with one or more images that include a format (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments, camera 202a that generates TLD data differs from other systems described herein incorporating cameras in that camera 202a can include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fish- eye lens, a lens having a viewing angle of approximately 120 degrees or more, and/or the like) to generate images about as many physical objects as possible.

[49] Light Detection and Ranging (LiDAR) sensors 202b include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). LiDAR sensors 202b include a system configured to transmit light from a light emitter (e.g., a laser transmitter). Light emitted by LiDAR sensors 202b include light (e.g., infrared light and/or the like) that is outside of the visible spectrum. In some embodiments, during operation, light emitted by LiDAR sensors 202b encounters a physical object (e.g., a vehicle) and is reflected back to LiDAR sensors 202b. In some embodiments, the light emitted by LiDAR sensors 202b does not penetrate the physical objects that the light encounters. LiDAR sensors 202b also include at least one light detector which detects the light that was emitted from the light emitter after the light encounters a physical object. In some embodiments, at least one data processing system associated with LiDAR sensors 202b generates an image (e.g., a point cloud, a combined point cloud, and/or the like) representing the objects included in a field of view of LiDAR sensors 202b. In some examples, the at least one data processing system associated with LiDAR sensor 202b generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like. In such an example, the image is used to determine the boundaries of physical objects in the field of view of LiDAR sensors 202b.

[50] Radio Detection and Ranging (radar) sensors 202c include at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Radar sensors 202c include a system configured to transmit radio waves (either pulsed or continuously). The radio waves transmitted by radar sensors 202c include radio waves that are within a predetermined spectrum In some embodiments, during operation, radio waves transmitted by radar sensors 202c encounter a physical object and are reflected back to radar sensors 202c. In some embodiments, the radio waves transmitted by radar sensors 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with radar sensors 202c generates signals representing the objects included in a field of view of radar sensors 202c. For example, the at least one data processing system associated with radar sensor 202c generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like. In some examples, the image is used to determine the boundaries of physical objects in the field of view of radar sensors 202c.

[51] Microphones 202d includes at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and/or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Microphones 202d include one or more microphones (e.g., array microphones, external microphones, and/or the like) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphones 202d include transducer devices and/or like devices. In some embodiments, one or more systems described herein can receive the data generated by microphones 202d and determine a position of an object relative to vehicle 200 (e.g., a distance and/or the like) based on the audio signals associated with the data.

[52] Communication device 202e includes at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, autonomous vehicle compute 202f, safety controller 202g, and/or DBW (Drive-By-Wire) system 202h. For example, communication device 202e may include a device that is the same as or similar to communication interface 314 of FIG. 3. In some embodiments, communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device that enables wireless communication of data between vehicles).

[53] Autonomous vehicle compute 202f include at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, safety controller 202g, and/or DBW system 202h. In some examples, autonomous vehicle compute 202f includes a device such as a client device, a mobile device (e.g., a cellular telephone, a tablet, and/or the like), a server (e.g., a computing device including one or more central processing units, graphical processing units, and/or the like), and/or the like. In some embodiments, autonomous vehicle compute 202f is the same as or similar to autonomous vehicle compute 400, described herein. Additionally, or alternatively, in some embodiments autonomous vehicle compute 202f is configured to be in communication with an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 1 14 of FIG. 1 ), a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 ), a V2I device (e.g., a V2I device that is the same as or similar to V2I device 1 10 of FIG. 1 ), and/or a V2I system (e.g., a V2I system that is the same as or similar to V2I system 118 of FIG. 1 ).

[54] Safety controller 202g includes at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, autonomous vehicle computer 202f, and/or DBW system 202h. In some examples, safety controller 202g includes one or more controllers (electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and/or the like). In some embodiments, safety controller 202g is configured to generate control signals that take precedence over (e.g., overrides) control signals generated and/or transmitted by autonomous vehicle compute 202f.

[55] DBW system 202h includes at least one device configured to be in communication with communication device 202e and/or autonomous vehicle compute 202f. In some examples, DBW system 202h includes one or more controllers (e.g., electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and/or the like). Additionally, or alternatively, the one or more controllers of DBW system 202h are configured to generate and/or transmit control signals to operate at least one different device (e.g., a turn signal, headlights, door locks, windshield wipers, and/or the like) of vehicle 200. [56] Powertrain control system 204 includes at least one device configured to be in communication with DBW system 202h. In some examples, powertrain control system 204 includes at least one controller, actuator, and/or the like. In some embodiments, powertrain control system 204 receives control signals from DBW system 202h and powertrain control system 204 causes vehicle 200 make longitudinal vehicle motion, such as to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a direction, decelerate in a direction or to make lateral vehicle motion such as performing a left turn, performing a right turn, and/or the like. In an example, powertrain control system 204 causes the energy (e.g., fuel, electricity, and/or the like) provided to a motor of the vehicle to increase, remain the same, or decrease, thereby causing at least one wheel of vehicle 200 to rotate or not rotate. In other words, steering control system 206 causes activities necessary for the regulation of the y-axis component of vehicle motion.

[57] Steering control system 206 includes at least one device configured to rotate one or more wheels of vehicle 200. In some examples, steering control system 206 includes at least one controller, actuator, and/or the like. In some embodiments, steering control system 206 causes the front two wheels and/or the rear two wheels of vehicle 200 to rotate to the left or right to cause vehicle 200 to turn to the left or right.

[58] Brake system 208 includes at least one device configured to actuate one or more brakes to cause vehicle 200 to reduce speed and/or remain stationary. In some examples, brake system 208 includes at least one controller and/or actuator that is configured to cause one or more calipers associated with one or more wheels of vehicle 200 to close on a corresponding rotor of vehicle 200. Additionally, or alternatively, in some examples brake system 208 includes an automatic emergency braking (AEB) system, a regenerative braking system, and/or the like.

[59] In some embodiments, vehicle 200 includes at least one platform sensor (not explicitly illustrated) that measures or infers properties of a state or a condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, a steering angle sensor, and/or the like. Although brake system 208 is illustrated to be located in the near side of vehicle 200 in FIG. 2, brake system 208 may be located anywhere in vehicle 200.

[60] Referring now to FIG. 3, illustrated is a schematic diagram of a device 300. As illustrated, device 300 includes processor 304, memory 306, storage component 308, input interface 310, output interface 312, communication interface 314, and bus 302. In some embodiments, device 300 corresponds to at least one device of vehicles 102 (e.g., at least one device of a system of vehicles 102), at least one device of remote AV system 114, fleet management system 1 16, V2I system 118, and/or one or more devices of network 112 (e.g., one or more devices of a system of network 1 12). In some embodiments, one or more devices of vehicles 102 (e.g., one or more devices of a system of vehicles 102 such as at least one device of remote AV system 114, fleet management system 116, and V2I system 118, and/or one or more devices of network 1 12 (e.g., one or more devices of a system of network 1 12) include at least one device 300 and/or at least one component of device 300. As shown in FIG. 3, device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.

[61] Bus 302 includes a component that permits communication among the components of device 300. In some cases, processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and/or the like), a microphone, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), and/or the like) that can be programmed to perform at least one function. Memory 306 includes random access memory (RAM), read-only memory (ROM), and/or another type of dynamic and/or static storage device (e.g., flash memory, magnetic memory, optical memory, and/or the like) that stores data and/or instructions for use by processor 304.

[62] Storage component 308 stores data and/or software related to the operation and use of device 300. In some examples, storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, and/or the like), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and/or another type of computer readable medium, along with a corresponding drive.

[63] Input interface 310 includes a component that permits device 300 to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and/or the like). Additionally or alternatively, in some embodiments input interface 310 includes a sensor that senses information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, an actuator, and/or the like). Output interface 312 includes a component that provides output information from device 300 (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), and/or the like).

[64] In some embodiments, communication interface 314 includes a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, and/or the like) that permits device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. In some examples, communication interface 314 permits device 300 to receive information from another device and/or provide information to another device. In some examples, communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and/or the like.

[65] In some embodiments, device 300 performs one or more processes described herein. Device 300 performs these processes based on processor 304 executing software instructions stored by a computer-readable medium, such as memory 305 and/or storage component 308. A computer-readable medium (e.g., a non-transitory computer readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes memory space located inside a single physical storage device or memory space spread across multiple physical storage devices.

[66] In some embodiments, software instructions are read into memory 306 and/or storage component 308 from another computer-readable medium or from another device via communication interface 314. When executed, software instructions stored in memory 306 and/or storage component 308 cause processor 304 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software unless explicitly stated otherwise.

[67] Memory 306 and/or storage component 308 includes data storage or at least one data structure (e.g., a database and/or the like). Device 300 is capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or the at least one data structure in memory 306 or storage component 308. In some examples, the information includes network data, input data, output data, or any combination thereof.

[68] In some embodiments, device 300 is configured to execute software instructions that are either stored in memory 306 and/or in the memory of another device (e.g., another device that is the same as or similar to device 300). As used herein, the term “module” refers to at least one instruction stored in memory 306 and/or in the memory of another device that, when executed by processor 304 and/or by a processor of another device (e.g., another device that is the same as or similar to device 300) cause device 300 (e.g., at least one component of device 300) to perform one or more processes described herein. In some embodiments, a module is implemented in software, firmware, hardware, and/or the like.

[69] The number and arrangement of components illustrated in FIG. 3 are provided as an example. In some embodiments, device 300 can include additional components, fewer components, different components, or differently arranged components than those illustrated in FIG. 3. Additionally or alternatively, a set of components (e.g., one or more components) of device 300 can perform one or more functions described as being performed by another component or another set of components of device 300.

[70] Referring now to FIG. 4A, illustrated is an example block diagram of an autonomous vehicle compute 400 (sometimes referred to as an “AV stack”). As illustrated, autonomous vehicle compute 400 includes perception system 402 (sometimes referred to as a perception module), planning system 404 (sometimes referred to as a planning module), localization system 406 (sometimes referred to as a localization module), control system 408 (sometimes referred to as a control module), and database 410. In some embodiments, perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included and/or implemented in an autonomous navigation system of a vehicle (e.g., autonomous vehicle compute 202f of vehicle 200). Additionally, or alternatively, in some embodiments perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included in one or more standalone systems (e.g., one or more systems that are the same as or similar to autonomous vehicle compute 400 and/or the like). In some examples, perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included in one or more standalone systems that are located in a vehicle and/or at least one remote system as described herein. In some embodiments, any and/or all of the systems included in autonomous vehicle compute 400 are implemented in software (e.g., in software instructions stored in memory), computer hardware (e.g., by microprocessors, microcontrollers, application-specific integrated circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and/or the like), or combinations of computer software and computer hardware. It will also be understood that, in some embodiments, autonomous vehicle compute 400 is configured to be in communication with a remote system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 1 14, a fleet management system 116 that is the same as or similar to fleet management system 116, a V2I system that is the same as or similar to V2I system 1 18, and/or the like).

[71] In some embodiments, perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system 402 receives image data captured by at least one camera (e.g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system 402 classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like). In some embodiments, perception system 402 transmits data associated with the classification of the physical objects to planning system 404 based on perception system 402 classifying the physical objects.

[72] In some embodiments, planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., routes 106) along which a vehicle (e.g., vehicles 102) can travel along toward a destination. In some embodiments, planning system 404 periodically or continuously receives data from perception system 402 (e.g., data associated with the classification of physical objects, described above) and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by perception system 402. In other words, planning system 404 may perform tactical function-related tasks that are required to operate vehicle 102 in on-road traffic. Tactical efforts involve maneuvering the vehicle in traffic during a trip, including but not limited to deciding whether and when to overtake another vehicle, change lanes, or selecting an appropriate speed, acceleration, deacceleration, etc. In some embodiments, planning system 404 receives data associated with an updated position of a vehicle (e.g., vehicles 102) from localization system 406 and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by localization system 406.

[73] In some embodiments, localization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area. In some examples, localization system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors 202b). In certain examples, localization system 406 receives data associated with at least one point cloud from multiple LiDAR sensors and localization system 406 generates a combined point cloud based on each of the point clouds. In these examples, localization system 406 compares the at least one point cloud or the combined point cloud to two- dimensional (2D) and/or a three-dimensional (3D) map of the area stored in database 410. Localization system 406 then determines the position of the vehicle in the area based on localization system 406 comparing the at least one point cloud or the combined point cloud to the map. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle. In some embodiments, maps include, without limitation, high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations thereof), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In some embodiments, the map is generated in real-time based on the data received by the perception system.

[74] In another example, localization system 406 receives Global Navigation Satellite System (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, localization system 406 receives GNSS data associated with the location of the vehicle in the area and localization system 406 determines a latitude and longitude of the vehicle in the area. In such an example, localization system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, localization system 406 generates data associated with the position of the vehicle. In some examples, localization system 406 generates data associated with the position of the vehicle based on localization system 406 determining the position of the vehicle. In such an example, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

[75] In some embodiments, control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle. In some examples, control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle by generating and transmitting control signals to cause a powertrain control system (e.g., DBW system 202h, powertrain control system 204, and/or the like), a steering control system (e.g., steering control system 206), and/or a brake system (e.g., brake system 208) to operate. For example, control system 408 is configured to perform operational functions such as a lateral vehicle motion control or a longitudinal vehicle motion control. The lateral vehicle motion control causes activities necessary for the regulation of the y-axis component of vehicle motion. The longitudinal vehicle motion control causes activities necessary for the regulation of the x-axis component of vehicle motion. In an example, where a trajectory includes a left turn, control system 408 transmits a control signal to cause steering control system 206 to adjust a steering angle of vehicle 200, thereby causing vehicle 200 to turn left. Additionally, or alternatively, control system 408 generates and transmits control signals to cause other devices (e.g., headlights, turn signal, door locks, windshield wipers, and/or the like) of vehicle 200 to change states.

[76] In some embodiments, perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, at least one transformer, and/or the like). In some examples, perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model alone or in combination with one or more of the above-noted systems. In some examples, perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment and/or the like). An example of an implementation of a machine learning model is included below with respect to FIGS. 4B-4D.

[77] Database 410 stores data that is transmitted to, received from, and/or updated by perception system 402, planning system 404, localization system 406 and/or control system 408. In some examples, database 410 includes a storage component (e.g., a storage component that is the same as or similar to storage component 308 of FIG. 3) that stores data and/or software related to the operation and uses at least one system of autonomous vehicle compute 400. In some embodiments, database 410 stores data associated with 2D and/or 3D maps of at least one area. In some examples, database 410 stores data associated with 2D and/or 3D maps of a portion of a city, multiple portions of multiple cities, multiple cities, a county, a state, a State (e.g., a country), and/or the like). In such an example, a vehicle (e.g., a vehicle that is the same as or similar to vehicles 102 and/or vehicle 200) can drive along one or more drivable regions (e.g., single-lane roads, multi-lane roads, highways, back roads, off road trails, and/or the like) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same as or similar to LiDAR sensors 202b) to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor.

[78] In some embodiments, database 410 can be implemented across a plurality of devices. In some examples, database 410 is included in a vehicle (e.g., a vehicle that is the same as or similar to vehicles 102 and/or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 1 14, a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 , a V2I system (e.g., a V2I system that is the same as or similar to V2I system 1 18 of FIG. 1 ) and/or the like.

[79] Referring now to FIG. 4B, illustrated is a diagram of an implementation of a machine learning model. More specifically, illustrated is a diagram of an implementation of a convolutional neural network (CNN) 420. For purposes of illustration, the following description of CNN 420 will be with respect to an implementation of CNN 420 by perception system 402. However, it will be understood that in some examples CNN 420 (e.g., one or more components of CNN 420) is implemented by other systems different from, or in addition to, perception system 402 such as planning system 404, localization system 406, and/or control system 408. While CNN 420 includes certain features as described herein, these features are provided for the purpose of illustration and are not intended to limit the present disclosure.

[80] CNN 420 includes a plurality of convolution layers including first convolution layer 422, second convolution layer 424, and convolution layer 426. In some embodiments, CNN 420 includes sub-sampling layer 428 (sometimes referred to as a pooling layer). In some embodiments, sub-sampling layer 428 and/or other subsampling layers have a dimension (i.e., an amount of nodes) that is less than a dimension of an upstream system. By virtue of sub-sampling layer 428 having a dimension that is less than a dimension of an upstream layer, CNN 420 consolidates the amount of data associated with the initial input and/or the output of an upstream layer to thereby decrease the amount of computations necessary for CNN 420 to perform downstream convolution operations. Additionally, or alternatively, by virtue of sub-sampling layer 428 being associated with (e.g., configured to perform) at least one subsampling function (as described below with respect to FIGS. 4C and 4D), CNN 420 consolidates the amount of data associated with the initial input.

[81] Perception system 402 performs convolution operations based on perception system 402 providing respective inputs and/or outputs associated with each of first convolution layer 422, second convolution layer 424, and convolution layer 426 to generate respective outputs. In some examples, perception system 402 implements CNN 420 based on perception system 402 providing data as input to first convolution layer 422, second convolution layer 424, and convolution layer 426. In such an example, perception system 402 provides the data as input to first convolution layer 422, second convolution layer 424, and convolution layer 426 based on perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle that is the same as or similar to vehicle 102), a remote AV system that is the same as or similar to remote AV system 1 14, a fleet management system that is the same as or similar to fleet management system 1 16, a V2I system that is the same as or similar to V2I system 118, and/or the like). A detailed description of convolution operations is included below with respect to FIG. 4G.

[82] In some embodiments, perception system 402 provides data associated with an input (referred to as an initial input) to first convolution layer 422 and perception system 402 generates data associated with an output using first convolution layer 422. In some embodiments, perception system 402 provides an output generated by a convolution layer as input to a different convolution layer. For example, perception system 402 provides the output of first convolution layer 422 as input to sub-sampling layer 428, second convolution layer 424, and/or convolution layer 426. In such an example, first convolution layer 422 is referred to as an upstream layer and sub-sampling layer 428, second convolution layer 424, and/or convolution layer 426 are referred to as downstream layers. Similarly, in some embodiments perception system 402 provides the output of sub-sampling layer 428 to second convolution layer 424 and/or convolution layer 426 and, in this example, sub-sampling layer 428 would be referred to as an upstream layer and second convolution layer 424 and/or convolution layer 426 would be referred to as downstream layers. [83] In some embodiments, perception system 402 processes the data associated with the input provided to CNN 420 before perception system 402 provides the input to CNN 420. For example, perception system 402 processes the data associated with the input provided to CNN 420 based on perception system 402 normalizing sensor data (e.g., image data, LiDAR data, radar data, and/or the like).

[84] In some embodiments, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer. In some examples, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer and an initial input. In some embodiments, perception system 402 generates the output and provides the output as fully connected layer 430. In some examples, perception system 402 provides the output of convolution layer 426 as fully connected layer 430, where fully connected layer 430 includes data associated with a plurality of feature values referred to as F1 , F2 . . . FN. In this example, the output of convolution layer 426 includes data associated with a plurality of output feature values that represent a prediction.

[85] In some embodiments, perception system 402 identifies a prediction from among a plurality of predictions based on perception system 402 identifying a feature value that is associated with the highest likelihood of being the correct prediction from among the plurality of predictions. For example, where fully connected layer 430 includes feature values F1 , F2, . . . FN, and F1 is the greatest feature value, perception system 402 identifies the prediction associated with F1 as being the correct prediction from among the plurality of predictions. In some embodiments, perception system 402 trains CNN 420 to generate the prediction. In some examples, perception system 402 trains CNN 420 to generate the prediction based on perception system 402 providing training data associated with the prediction to CNN 420.

[86] Referring now to FIGS. 4C and 4D, illustrated is a diagram of example operation of CNN 440 by perception system 402. In some embodiments, CNN 440 (e.g., one or more components of CNN 440) is the same as, or similar to, CNN 420 (e.g., one or more components of CNN 420) (see FIG. 4B).

[87] At step 450, perception system 402 provides data associated with an image as input to CNN 440 (step 450). For example, as illustrated, perception system 402 provides the data associated with the image to CNN 440, where the image is a greyscale image represented as values stored in a two-dimensional (2D) array. In some embodiments, the data associated with the image may include data associated with a color image, the color image represented as values stored in a three-dimensional (3D) array. Additionally, or alternatively, the data associated with the image may include data associated with an infrared image, a radar image, and/or the like.

[88] At step 455, CNN 440 performs a first convolution function. For example, CNN 440 performs the first convolution function based on CNN 440 providing the values representing the image as input to one or more neurons (not explicitly illustrated) included in first convolution layer 442. In this example, the values representing the image can correspond to values representing a region of the image (sometimes referred to as a receptive field). In some embodiments, each neuron is associated with a filter (not explicitly illustrated). A filter (sometimes referred to as a kernel) is representable as an array of values that corresponds in size to the values provided as input to the neuron. In one example, a filter may be configured to identify edges (e.g., horizontal lines, vertical lines, straight lines, and/or the like). In successive convolution layers, the filters associated with neurons may be configured to identify successively more complex patterns (e.g., arcs, objects, and/or the like).

[89] In some embodiments, CNN 440 performs the first convolution function based on CNN 440 multiplying the values provided as input to each of the one or more neurons included in first convolution layer 442 with the values of the filter that corresponds to each of the one or more neurons. For example, CNN 440 can multiply the values provided as input to each of the one or more neurons included in first convolution layer 442 with the values of the filter that corresponds to each of the one or more neurons to generate a single value or an array of values as an output. In some embodiments, the collective output of the neurons of first convolution layer 442 is referred to as a convolved output. In some embodiments, where each neuron has the same filter, the convolved output is referred to as a feature map.

[90] In some embodiments, CNN 440 provides the outputs of each neuron of first convolutional layer 442 to neurons of a downstream layer. For purposes of clarity, an upstream layer can be a layer that transmits data to a different layer (referred to as a downstream layer). For example, CNN 440 can provide the outputs of each neuron of first convolutional layer 442 to corresponding neurons of a subsampling layer. In an example, CNN 440 provides the outputs of each neuron of first convolutional layer 442 to corresponding neurons of first subsampling layer 444. In some embodiments, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of first subsampling layer 444. In such an example, CNN 440 determines a final value to provide to each neuron of first subsampling layer 444 based on the aggregates of all the values provided to each neuron and an activation function associated with each neuron of first subsampling layer 444.

[91] At step 460, CNN 440 performs a first subsampling function. For example, CNN 440 can perform a first subsampling function based on CNN 440 providing the values output by first convolution layer 442 to corresponding neurons of first subsampling layer 444. In some embodiments, CNN 440 performs the first subsampling function based on an aggregation function. In an example, CNN 440 performs the first subsampling function based on CNN 440 determining the maximum input among the values provided to a given neuron (referred to as a max pooling function). In another example, CNN 440 performs the first subsampling function based on CNN 440 determining the average input among the values provided to a given neuron (referred to as an average pooling function). In some embodiments, CNN 440 generates an output based on CNN 440 providing the values to each neuron of first subsampling layer 444, the output sometimes referred to as a subsampled convolved output.

[92] At step 465, CNN 440 performs a second convolution function. In some embodiments, CNN 440 performs the second convolution function in a manner similar to how CNN 440 performed the first convolution function, described above. In some embodiments, CNN 440 performs the second convolution function based on CNN 440 providing the values output by first subsampling layer 444 as input to one or more neurons (not explicitly illustrated) included in second convolution layer 446. In some embodiments, each neuron of second convolution layer 446 is associated with a filter, as described above. The filter(s) associated with second convolution layer 446 may be configured to identify more complex patterns than the filter associated with first convolution layer 442, as described above.

[93] In some embodiments, CNN 440 performs the second convolution function based on CNN 440 multiplying the values provided as input to each of the one or more neurons included in second convolution layer 446 with the values of the filter that corresponds to each of the one or more neurons. For example, CNN 440 can multiply the values provided as input to each of the one or more neurons included in second convolution layer 446 with the values of the filter that corresponds to each of the one or more neurons to generate a single value or an array of values as an output.

[94] In some embodiments, CNN 440 provides the outputs of each neuron of second convolutional layer 446 to neurons of a downstream layer. For example, CNN 440 can provide the outputs of each neuron of first convolutional layer 442 to corresponding neurons of a subsampling layer. In an example, CNN 440 provides the outputs of each neuron of first convolutional layer 442 to corresponding neurons of second subsampling layer 448. In some embodiments, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregates of all the values provided to each neuron of second subsampling layer 448. In such an example, CNN 440 determines a final value to provide to each neuron of second subsampling layer 448 based on the aggregates of all the values provided to each neuron and an activation function associated with each neuron of second subsampling layer 448.

[95] At step 470, CNN 440 performs a second subsampling function. For example, CNN 440 can perform a second subsampling function based on CNN 440 providing the values output by second convolution layer 446 to corresponding neurons of second subsampling layer 448. In some embodiments, CNN 440 performs the second subsampling function based on CNN 440 using an aggregation function. In an example, CNN 440 performs the first subsampling function based on CNN 440 determining the maximum input or an average input among the values provided to a given neuron, as described above. In some embodiments, CNN 440 generates an output based on CNN 440 providing the values to each neuron of second subsampling layer 448. [96] At step 475, CNN 440 provides the output of each neuron of second subsampling layer 448 to fully connected layers 449. For example, CNN 440 provides the output of each neuron of second subsampling layer 448 to fully connected layers 449 to cause fully connected layers 449 to generate an output. In some embodiments, fully connected layers 449 are configured to generate an output associated with a prediction (sometimes referred to as a classification). The prediction may include an indication that an object included in the image provided as input to CNN 440 includes an object, a set of objects, and/or the like. In some embodiments, perception system 402 performs one or more operations and/or provides the data associated with the prediction to a different system, described herein.

[97] Referring now to FIGS. 5A-5C, illustrated are diagrams of a non-dynamic antenna system 500. For example, a non-dynamic antenna system is an antenna system with one or more static antennas. As shown in FIG. 5A, the non-dynamic antenna system 500 includes a plurality of antennas 502A, 502B, 502C, 502D. As shown in Fig. 5B, antenna 502A is soldered to a printed circuit board (PCB) 504, which is permanently installed within the non-dynamic antenna system 500. Once the antennas 502A, 502B, 502C, 502D are built and soldered to the PCB, their orientation or position cannot physically change. FIG. 5C is diagram of a non-dynamic antenna system 500 further including an electronic control unit 510 having modems 512A, 512B. As shown, once an antenna is designated to a modem (e.g., antennas 502A and 502C connected to modem 512A, and antennas 502B and 502D connected to modem 512B), the connection cannot be changed. Accordingly, the non-dynamic antenna system 500 includes antennas which are static and mounted in a permanent position, which can lead to connection problems due to their lack of flexibility (e.g., adjustability). Further, during service, the wireless connection may degrade or fail, and there is little room for actions to improve the connection, impacting remote vehicle assistance (RVA) and Mission Services Platform (MSP) functionalities.

[98] The present disclosure relates to antenna systems, vehicles, and systems that provide for a dynamic capability of the antennas, such as via controlling a movement of the antennas and/or phase and/or angle of the antenna(s), as well as via switching connection of the antennas with a modem. The disclosed antenna system can use dynamic actions to achieve improved connection with network nodes, such as for operation of an autonomous vehicle. In one or more examples, the antennas may be movable, allowing for the antennas to reposition for better signal coverage. In some cases, the antenna system includes the capability of automatically controlling the rotation, orientation, phase, and/or angle of an individual antenna of the antenna system during service (or during the training process of a model, e.g., a machine learning model) to scan for a better wireless connection and/or the best wireless connection, orientation, phase, cell tower, etc. In or more examples, the top-performing antennas can be connected to the modem while underperforming antennas can be disconnected. Instead of having unchangeable connection between each antenna and the modems, switches can be used on the hardware side to enable the modems to choose which antennas to connect to automatically. For example, a first modem may be connected to a first antenna in some areas, but in other areas the first modem can change the connection and connect to another antenna (e.g., a second antenna) that allows for improved signal coverage. Both implementations can be used at the same time and/or separately. In some embodiments, a machine learning model and/or artificial intelligence can be used for providing antenna orientation, positioning, connection, and angle recommendations, as discussed with respect to FIGS. 11 -12. In some examples, the machine learning model can be trained during a training period and then used during a control or service period to control the orientation of one or more antennas and their connections to one or more modes, e.g., based one or more of location and/or orientation of a vehicle controlled using the antenna system, and a carrier and/or network node with which the one or more antennas are communicating.

[99] Referring now to FIGS. 6, 7A-7B, 8A-8B, illustrated are diagrams of an implementation of an antenna system, such as a dynamic antenna system (e.g., an antenna array module). In some embodiments, antenna system 600, 700, 800 is connected with and/or incorporated in a vehicle (e.g., an autonomous vehicle that is the same as, or similar to, vehicle 200 of Fig. 2). In one or more embodiments or examples, antenna system 600, 700, 800 can be in communication with an AV (e.g., such as Autonomous System 202 illustrated in FIG. 2, device 300 of FIG. 3), an AV system, an AV compute (such as AV compute 202f of FIG. 2 and/or AV compute 400 of FIG. 4), a remote AV system (such as remote AV system 114 of FIG. 1 ), a fleet management system (such as fleet management system 1 16 of FIG. 1 ), and a V2I system (such as V2I system 1 18 of FIG. 1 ). The antenna system 600, 700, 800, can be used for operating an autonomous vehicle. The antenna system 600, 700, 800, may not be for operating an autonomous vehicle.

[100] In one or more embodiments or examples, the antenna system 600, 700, 800 can be in communication with one or more of: a device (such as device 300 of FIG. 3), a localization system (such as localization system 406 of FIG. 4), a planning system (such as the planning system 404 of FIG. 4), a perception system (such as the perception system 402 of FIG. 4), and a control system (such as the control system 408 of FIG. 4) .

[101] A number of antenna systems are disclosed herein. It will be understood that each antenna system 600, 700, 800 discussed, may include any and/or all elements or features of the other antenna systems 600, 700, 800 of the disclosure. Certain discussion of antenna systems 600, 700, 800 are made for clarity. The discussion of any of the antennas 602A, 602B, 602C, 602D (generically referred to as antenna(s) 602), 702, 802, can be incorporated into any antenna system 600, 700, 800. The discussion of any of the modems 612A, 612B (generically referred to as modem(s) 612) can be incorporated into any antenna system 600, 700, 800, and may be used with any at least one antenna 602, 702, 802.

[102] An antenna system for an autonomous vehicle is disclosed. The antenna system 600 includes at least one modem 612. The antenna system 600 includes at least one antenna 602 operatively connected with the at least one modem 612. The antenna system 600 is configured to determine, using at least one processor, a performance parameter indicative of a performance of a communication between the at least one antenna 602 and a network node. The antenna system 600 is configured to control, using the at least one processor, based on the performance parameter, one or more of a position (e.g., angular position, translational position, and/or orientation) of the at least one antenna 602, and a connection of the at least one antenna 602 to the at least one modem 612. The antenna system 600 is configured to control, using the at least one processor, based on the performance parameter, a position of the at least one antenna 602. The antenna system 600 is configured to control, using the at least one processor, based on the performance parameter, a connection of the at least one antenna 602 to the at least one modem 612.

[103] Referring now to FIG. 6, illustrated are diagrams of an implementation of antenna system 600. The antenna system 600 can use available rooftop space to combine different antennas into a single housing, such as for allowing for an improved RF performance while still supporting manufacturability and desired design language. In one or more examples or embodiments, the antenna system 600 is located (e.g., attached, associated with, connected to) on a roof of an autonomous vehicle, such as vehicle 200 of FIG. 2. A single housing can ease manufacturing considerably. Moreover, a single rooftop housing can be easily upgrade-able, such as to support future 5G cellular needs.

[104] As shown, in one or more embodiments or examples, the antenna system 600 includes at least one antenna 602 (designated as different antennas 602A, 602B, 602C, 602D). In one or more embodiments or examples, the at least one antenna 602 is configured to connect (e.g., wirelessly connected) with network nodes (such as devices having network nodes). In one or more embodiments or examples, the antenna system 600 includes a plurality of antennas, such as plurality of antennas 602A, 602B, 602C, 602D shown in FIG. 6. The at least one antenna 602 can be any number of different antenna types, such as a cellular antenna and/or a vehicle-to-everything (V2X) antenna. In one or more embodiments or examples, the at least one antenna 602 is located within a housing.

[105] In one or more embodiments or examples, the at least one antenna 602 can inlcude one or more of a cellular antenna and a V2X (e.g., vehicle to everything) antenna. However, the particular antenna type is not limiting. In one or more embodiments or examples, the at least one antenna 602 can include one or more of a long-term evolution (LTE) antenna, a 4G antenna, a 5G antenna, a millimeter-wave antenna, a new radio (NR) antenna and 6G antenna. In some cases, the at least one antenna 602 can be configured to function, such as transmit, receive, and/or obtain data, with one or more networks, such as a cellular network and/or a cellular system, and can be considered a cellular antenna. The one or more networks can be for different carriers, such as different cellular carriers. [106] In one or more embodiments or examples, the at least one antenna 602 is configured to cover a frequency spectrum, such as ranging from 600MHz to 6000MHz, such as a full frequency spectrum. However, the spectrum is not limiting. In one or more embodiments or examples, each antenna of the at least one antenna 602 can be configured to cover a portion of the frequency spectrum. In one or more embodiments or examples, each antenna of the at least one antenna 602 can be configured to cover the same frequency spectrum. Antennas of the at least one antenna 602 can be configured to cover some overlapping portions of the frequency spectrum.

[107] In one or more examples or embodiments, the antenna system 600 includes a plurality of antennas, such as plurality of antennas 602A, 602B, 602C, 602D, associated with at least two different carriers. In one or more examples or embodiments, each carrier of the at least two different carriers operate on a different frequency band. For example, a first antenna of the at least one antenna 602 communicates with a first carrier operating on a first frequency band and a second antenna of the at least one antenna 602 communicates with a second carrier operating on a second frequency band. This can allow the antenna system 600 to communicate with different carriers during operation of the autonomous vehicle. The antenna system 600 can be configured to communicate with a plurality of different carriers during operation of the autonomous vehicle.

[108] In one or more embodiments or examples, the at least one antenna 602 is a part of, or form part of, a vehicular communication system. For example, the at least one antenna 602 is one or more of a vehicle-to-everything (V2X) antenna, a vehicle-to- vehicle (V2V) antenna, a vehicle-to-infrastructure (V2I) antenna, and a vehicle-to- pedestrian (V2P) antenna. For example, the at least one antenna 602 is an antenna configured to communicate using a vehicular communication system, e.g., communication device 202e discussed with respect to FIG. 2. For example, the at least one antenna 602 is configured to communicate, such as transmit data, and/or receive data, to and from other connectable devices and/or equipment within a particular area.

[109] For example, in FIG. 6, antennas 602A and 602C are V2X antennas and antennas 602B and 602D are cellular antennas. Different iterations of antennas can be used, and the particular type and/or number of antennas is not limiting. In one or more embodiments or examples, the at least one antenna 602 includes cellular antennas and V2X antennas.

[110] In one or more examples, as shown in FIG. 6, the at least one antenna 602 is operatively connected (e.g., communicatively coupled, operatively coupled, in communication with, connected to with or without intermediate components, configured to provide data between) with at least one modem 612 (designated as different modems 612A, 612B). The at least one antenna 602 is operatively connected with at least one modem 612 via, for example, a wired connection. In one or more embodiments or examples, the antenna system 600 includes a plurality of modems. The at least one modem 612 can be first modem 612A and second modem 612B, but further modems can be used as well. In one or more examples, the at least one modem 612 is associated with an electric control unit 610. In one or more examples, the at least one modem 612 is located in a housing, for example located in a housing with the at least one antenna 602. Each antenna of the at least one antenna 602 can be connected to a modem of the at least one modem 612. Each antenna of the at least one antenna 602 can be connected to each modem of the at least one modem 612. In one or more embodiments or examples, the at least one modem 612 provides access to a network, such as a cellular network, a V2X network, and/or Internet.

[111] In one or more embodiments or examples, the antenna system 600 is configured to control one or more aspects of the antenna system 600 in order to improve (e.g., increase) performance of the at least one antenna 602. For example, the antenna system 600 is configured to control a position of the at least one antenna 602, and/or a connection of the at least one antenna 602 to the at least one modem 612, based on a performance parameter.

[112] In one or more examples or embodiments, the performance parameter is indicative of a performance of a communication between the at least one antenna 602 and a network node. The network node is, for example, a cellular carrier network node and/or a V2X network node. For example, the network node includes one or more of: a radio access network node, a base station, and an access point. In one or more examples or embodiments, the performance parameter is indicative of one or more of: a signal to noise ratio (SNR), interference levels, connection strength, signal degradation, signal disconnection, and wireless communication strength. In one or more examples or embodiments, the performance parameter includes one or more of: a parameter indicative of a connection bandwidth, latency, SINR (Signal to Interference & Noise Ratio), RSSI (Received Signal Strength Indicator), RSRQ (Reference Signal Received Quality) and RSRP (Reference Signal Received Power).

[113] For example, the antenna system 600 can take one or more actions discussed herein in an attempt to fix any impairments and degradation in performance of the at least one antenna 602.

[114] In one or more examples or embodiments, the antenna system 600 is configured to control, using the at least one processor, based on the performance parameter, the position (e.g., rotational position, translational position, and/or orientation) of the at least one antenna 602 and/or the connection of the at least one antenna 602 to the at least one modem 612 by determining if the performance parameter satisfies a criterion. In one or more examples or embodiments, in response to determining that the performance parameter satisfies the criterion (e.g., a malfunction or poor function criterion), the antenna system 600 is configured to control the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem. In one or more examples or embodiments, in response to determining that the performance parameter does not satisfy the criterion, the antenna system 600 is configured to not control the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem.

[115] In other words, if the performance parameter satisfies the criterion, the antenna system 600, specifically the at least one antenna 602, may not be operating properly and/or may have some performance issue, such as having a limited capacity. For example, the criterion may be satisfied when the strength or the signal-to-noise ratio (SNR) of a signal generated by the antenna is below a threshold value. In some cases, the signal may be associated with a wireless signal received from a network node and the criterion can be different for different network nodes. This may lead to issues with operating an autonomous vehicle, and it can be advantageous to rectify the performance issue by controlling the position of the at least one antenna 602 and/or the connection of the at least one antenna 602 to the at least one modem 612. [116] In one or more embodiments or examples, the criterion can be associated with or based on one or more of: a signal to noise ratio (SNR), interference levels, connection strength, signal degradation, signal disconnection, and wireless communication strength (e.g., a received signal strength). In one or more embodiments or examples, the criterion is indicative of a radio performance. For example, the criterion can be set at a particular value that does not change during operation of the antenna system 600. In one or more embodiments or examples, the criterion is indicative of a comparative performance. The criterion can then adjust during operation based on improvement of the condition(s) of the antenna of the antenna system, such as location, and/or wireless channel condition, etc. For example, if the antenna system 600 is in a generally low signal strength zone, it may not be advantageous for the antenna system 600 to continuously attempt to mitigate the issue that may not be able to be resolved. In some examples, the criterion can include a signal parameter indicating that SNR of a received signal is below a threshold value, the interference level of the received signal is above a threshold value, the strength of a received signal is below threshold value, a distortion and/or degradation of a signal is above a threshold value. In some examples, the criterion may indicate one or more signal parameters that quantify the strength and quality of a received signal are above or below threshold values. In some cases, a threshold value for a signal parameter can be a value stored in the memory of the system. In some cases, a threshold value for a signal parameter can be a predetermined value provided by a user. In some cases, a threshold value can be adjustable before or during a navigation or training period. In some cases, a threshold value for a signal parameter can be adjusted (e.g., adaptively adjusted) based on a location of the antenna system, a network node or carrier associated with a received signal (or previously received signal). In some cases, a threshold value for a signal parameter may be adaptively adjusted based on other factors and parameters.

[117] In one or more examples or embodiments, the performance parameter satisfies the criterion in response to the performance parameter being below a performance threshold. In one or more examples or embodiments, the performance parameter does not satisfy the criterion in response to the performance parameter not being below (e.g., being equal or above) a performance threshold. As an example, the performance threshold can be a particular signal strength. For example, if the performance parameter indicates a signal strength that is below the performance threshold given by the particular signal strength, the antenna system 600 determines that the performance parameter satisfies the criterion. Accordingly, the antenna system 600 can take an action to improve the signal quality, e.g., by controlling the position (e.g., angular position, or location with respect to a reference coordinate of the antenna system 600) of the antenna and/or the connection of the antenna to the modem. In some cases, the reference coordinate of the antenna system 600 can be fixed with respect to a housing of the antenna system 600.

[118] As mentioned above, the antenna system 600 may be configured to control certain aspects in order to improve performance of the antenna system 600 based on the performance parameter. Some implementations are discussed herein, which include controlling a position of the at least one antenna 602 and controlling a connection between the at least one antenna 602 and the at least one modem 612. The implementations can be used separately or together. In some examples, other aspect may be controlled, e.g., an amplification or pre-amplification of a signal received by an antenna, polarization of an antenna, and the like.

[119] In some embodiments, the antenna system 600 can be configured to control, using at least one processor, the position of the at least one antenna 602 and/or the connection of the at least one antenna 602 to the at least one modem 612 based on control signals received from a control sub-system of the antenna system 600. In some such embodiments, the control sub-system can use a machine learning model to generate the control signals based at least in part on a first signal indicative of a location of the antenna system 600 in an area and a second signal indicative of a carrier associated with a node width which the at least one antenna 602 is communicating. In some cases, the antenna system 600 may be used to train the machine learning model during a training period when antenna system 600 moves within the area. In some cases, during the training period, a fleet of vehicles carrying antenna systems similar to the antenna system 600 may navigate in the same area and the data collected by the antenna systems during such period may be used to train the machine learning model (e.g., determine one or more parameters of the machine learning model). Subsequently, the trained machine learning model may be used by an antenna system 600 of a vehicle during a service or navigation period for assisted or autonomous navigation of the vehicle in the area. For example, at some or all points (each point) in the area, the antenna system 600 may control the orientation of an antenna or the connection of the antenna element to a modem, according to the control signals generated by the machine learning model that was previously trained for navigation in the same area.

[120] In one or more examples, as a first implementation, the antenna system 600 controls certain physical aspects of the at least one antenna 602. In one or more examples or embodiments, the antenna system 600 is configured to control the position of the at least one antenna 602 relative to a position of the at least one modem 612 or a reference frame associated with the antenna system (e.g., a static reference frame with respect to a housing of the antenna system 600). In one or more embodiments or examples, the antenna system 600 is configured to control the position of the at least one antenna 602 by one or more of: repositioning, translating, rotating, tilting, and otherwise moving the at least one antenna 602. Controlling a position of the at least one antenna 602 can include any movement of the at least one antenna 602. For example, the antenna system 600 is configured to control a relative position of the at least one antenna 602. Changing position of the at least one antenna 602 may provide better connection, such as coverage, to a known cellular tower. The antenna system 600, for example, controls the position of the at least one antenna to scan for the best connection, orientation, phase, cell tower, etc., which provides for improved performance.

[121] In one or more examples or embodiments, the antenna system 600 is configured to control, using the at least one processor, based on the performance parameter, a position of the at least one antenna 602 by controlling one or more of: an orientation of the at least one antenna, a phase of the at least one antenna, an angle (e.g., tilt) of the at least one antenna, and a pose of the at least one antenna. Controlling a position of the at least one antenna 602 can include one or more potential changes to the position of the at least one antenna 602. The pose can include a position and orientation.

[122] In one or more examples or embodiments, the antenna system 600 is configured to control, using the at least one processor, based on the performance parameter, the position of the at least one antenna 602 by controlling a rotation of the at least one antenna 602. In other words, the antenna system 600 is configured to rotate the at least one antenna 602. For example, the antenna system 600 includes rotation member 604, (referred to individually as rotation members 604A, 604B, 604C, 604D, and collectively as rotation member 604 of FIG. 6). An example of controlling the position of the at least one antenna 602 is shown in FIGS. 7A-7B, which shows a top-down view of a diagram of an implementation of antenna system 700. For example, the antenna system 600 is configured to rotate the at least one antenna 602, such as around a rotation axis (extending out of the page). Other axis of rotation can be used as well, and the antenna system 600 can be configured to rotate the at least one antenna 602 on a plurality of axes of rotation.

[123] FIGS. 7A-7B illustrate an example antenna system 700 allowing for control of rotation of the at least one antenna 702. As shown, each antenna (702A, 702B, 702C, 702D, similar to at least one antenna 702, referred to individually as antenna 602A, 602B, 602C, 602D, in FIG. 6) of the at least one antenna 702 can be associated with (e.g., attached, integral with, connected to) a rotation member 704 (704A, 704B, 704C, 704D, similar to rotation member 604, referred to individually as rotation members 604A, 604B, 604C, 604D, in FIG. 6). The rotation member 704 can be, for example, a platform that the at least one antenna 702 is located on. The rotation member 704 can be configured for rotation, such as about a vertical axis. Each at least one antenna 702 may be associated with a separate rotation member 704, such as shown in FIG. 7A. A single rotation member 704 may be associated with a plurality of the at least one antenna 702, such as all of the antennas. For example, using a single rotation member 704 for multiple antennas can allow for consistent phase changes of the antennas on the rotation member 704. The rotation member 704 can allow for any type of rotation, such as 360 degree rotation, of the at least one antenna 702.

[124] FIG. 7B illustrates an example rotation member 704 that can be used for controlling a rotation of the at least one antenna 702 of the antenna system 700. As shown, the rotation member 704 is optionally associated with a slip ring 706. For example, the slip ring 706 and/or the rotation member 704 is connected to a motor 710, such as via a shaft 708. In some cases, a motor 710 is, for example, associated with the antennas of the at least one antenna 702. Each antenna of the at least one antenna 702 may be associated with a separate motor 710. In one or more examples or embodiments, the antenna system 700 is configured to rotate the at least one antenna 702 at particular preset angles and/or phases.

[125] In some cases, a preset angle and/or phase can be stored in a memory of the antenna system 700. In some cases, a preset angle can be associated with an orientation and/or a location of the antenna system 700 (and thereby with an orientation and/or a location of a vehicles that carries the antenna system 700). In some cases, the preset angle can be further associated with a specific carrier or network node. In some cases, a preset angle can be determined by a machine learning model (e.g., a previously trained machine learning model) based at least in part on the location of the antenna system 700 and a network node or carrier with which the at least one antenna 702 is communicating, and, in some examples, additionally the orientation of the antenna system 700.

[126] In one or more examples, the antenna system 700 is configured to rotate the at least one antenna 702 automatically. For example, the antenna system 700 rotates the at least one antenna 702 until the performance parameter does not satisfy the criterion as performance has been re-established. In other words, the antenna system 700 can be configured to rotate the at least one antenna 702 until performance improves.

[127] In one or more examples, the antenna system 700 is configured to control a position of the at least one antenna 702 in manners other than rotation. An example of controlling the position of the at least one antenna 702 in another fashion is shown in FIGS. 8A-8B, which shows diagrams of an implementation of antenna system 800. Antenna system 800 can be particularly advantageous for V2X antennas. The mechanism of controlling the position of the at least one antenna 802 can be used separately or in conjunction with the rotation member, such as rotation member 604 of FIG. 6 and 704 of FIGS. 7A-7B.

[128] In some examples, a V2X device, such as having a network node 804, is mounted higher above the antenna level. When an antenna position and/or angle is fixed, the performance parameter may indicate a degradation in the radio performance of the at least one antenna in that fixed position and/or angle. As shown in FIG. 8B, the antenna system 800 is, for example, configured to tilt (e.g., change the angle of) the at least one antenna 802 to improve the connection to the network node 804 (e.g., for achieving Line of Sight). For example, the antenna system 800 can control an angle of the at least one antenna 802 with a vertical axis. The at least one antenna 802 can be associated with a mechanical member, such as a rotatable joint, allowing for tilt of the at least one antenna 802.

[129] As a second implementation, returning to FIG. 6, the antenna system 600 is, in some examples, configured to control connections between the at least one antenna 602 and the at least one modem 612. In one or more examples or embodiments, the antenna system 600 is configured to control, using the at least one processor, based on the performance parameter, the connection of the at least one antenna 602 to the at least one modem 612. For example, the antenna system 600 connects and disconnects (e.g., couples and uncouples) the at least one antenna 602 from the at least one modem 612. This control of connection is performed by, for example, opening or closing a switch controlled by the antenna system 600. By controlling a connection of the at least one antenna 602 to the at least one modem 612, the antenna system 600 can enable a choice of which antennas of the at least one antenna 602 to connect to which modem of the at least one modem 612. For example, the antenna system 600 is configured to connect the top-performing antenna of the at least one antenna 602 to a modem of the at least one modem 612 using the same carrier. This may allow for an improvement of signal quality between the antenna system 600 and a network node the antenna system 600 is communicating with.

[130] As an example, an autonomous vehicle, such as the antenna system 600, may determine the locations of cell towers for each carrier. The orientation and location of the autonomous vehicle can also be determined. Some antennas of the at least one antenna 602 can provide an improved performance than others, and the antenna system 600 can couple the relevant at least one modem 612 to the antenna which shows an improved performance. In certain circumstances, the antenna system 600 may completely lose cellular coverage in the current connection, and the antenna system 600 may control the connection between the at least one antenna 602 and the at least one modem 612 to rectify the issue. [131] In one or more examples or embodiments, the antenna system 600 is configured to control the connection of the at least one antenna 602 to the at least one modem 612 using a switch 614 coupled to the at least one antenna 602 and the at least one modem 612. In FIG. 6, the switch 614 is designated as 614A1 , 614A2, 614A3, 614A4 for connection with modem 612A and 614B1 , 614B2, 614B3, 614B4 for connection with modem 612B. The switches 614 can be associated with the electronic control unit 610. The particular location of the switches 614 shown in FIG. 6 is not limiting.

[132] In one or more examples or embodiments, the antenna system 600 controls operation of each respective switch 614 in order to control a connection of the at least one antenna 602 to the at least one modem 612, such as based on the performance parameter. As shown in FIG. 6, the antenna system 600 can include a switch 614A1 , 614A2, 614A3, 614A4 for every connection between an antenna 602A, 602B, 602C, 602D and the modem 612A, and a switch 614B1 , 614B2, 614B3, 614B4 for every connection between an antenna 602A, 602B, 602C, 602D and the modem 612B. As shown in FIG. 6, antenna 602C and antenna 602B are connected to modem 612B and antenna 602D and antenna 602A are connected to modem 612A.

[133] In one or more examples or embodiments, the antenna system 600 includes a plurality of modems and a plurality of antennas. In one or more examples or embodiments, each of the plurality of antennas is connected to each of the plurality of modems via a switch 614. FIG. 6 illustrates a plurality of antennas 602A, 602B, 602C, 602D each connected to the modems 612A, 612B via switches 614A1 , 614A2, 614A3, 614A4, 614B1 , 614B2, 614B3, 614B4. This can enable the antenna system 600, for every modem, to select which antennas to connect to through the switches.

[134] FIGS. 9A-9B are diagrams of antenna system as seen by an autonomous vehicle 902 having an antenna system 600, 700, or 800 as disclosed herein. In particular, FIG. 9A is a diagram of a theoretical (e.g., estimated or calculated) antenna coverage area (e.g., shape) 904, whereas FIG. 9B is a diagram of actual antenna coverage area 908A. In some cases, the autonomous vehicle 902 is able to connect to network nodes 906A, and 906B that are close or within the boundary of the coverage area 908A. The autonomous vehicle 902 can be subject to losing at least some cellular coverage as shown in FIG. 9B, and not being able to connect to the network node 906C as it is outside of the coverage area 908A of the antenna system (far from the boundary of the coverage area 908A). For example, the network node 906C may be blocked, or may just not be within the coverage area 908A of the antenna system of the autonomous vehicle 902. When the autonomous vehicle 902 uses a static antenna system (e.g., antenna system 500), this can be a significant problem for the autonomous vehicle 902, as a loss of connection may lead to a loss of control. Further, while autonomous vehicle 902 may be able to connect with network node 906A, as it is on the edge of the coverage area 908A the connection may be weak.

[135] Advantageously, the antenna system 600, 700, 800 disclosed herein can allow for connection to network node 906C and/or improvement of the connection to network node 906A. For example, the antenna system 600, 700, 800 can effectively change the coverage area, by adjusting the position (e.g., the orientation and/or location) and/or a phase of one or more antennas, to improve connection with the network nods 906a, 906B and establish connection with network node 906C.

[136] As the antenna system 600, 700, 800 can be configured to control a position of the at least one antenna or a connection of the at least one antenna to the at least one modem, based on the performance parameter, it may change the coverage area 908A (e.g., by changing a boundary, size, and/or shape of the coverage area 908A), such that the network nodes 906A and 906C are included in the change coverage area 908A and a reliable communication link can be established with these network nodes.

[137] FIG. 10 is a diagram of an example implementation of a dynamic antenna system. In the example shown, the autonomous vehicle 910 includes a dynamic antenna system (e.g., the antenna system 600, 700, 800). In some cases, the autonomous vehicle 910 can be positioned similar to the autonomous vehicle 902 as shown in FIG. 9B, which previously was unable to connect with network node 906C and/or had a weak connection to network node 906A.

[138] In some cases, by controlling the position or the connection, the antenna system 600, 700, 800 can modify the coverage from coverage area 908A (shown in FIG. 9B) to coverage area 908B (shown in FIG. 10). As illustrated in FIG. 10, both network node 906A and network node 906C are located within the coverage 908B, providing for improved connection for the autonomous vehicle 910. As is also show, the network node 906B is not located within the coverage area 908B.

[139] In some cases, the antenna system may determine that coverage area 908B provides a better overall cellular connection (e.g., because of the improved connection with the network node 906A and/or 906C) compared to another coverage area that includes the network node 906B but establishes a weaker connection with the network nodes 906A and/or 906C. As such in the example shown, the network node 906B is not included in the coverage area 908B. It should be understood that a change made to a coverage area may be constrained at least by the number of adjustable antennas in the antenna system and the performance limitation of each antenna. In some examples, the antenna system can be configured to change the coverage area to establish the strongest and/or the most reliable cellular connection within such constraints.

[140] FIG. 11 illustrates a diagram of an example antenna system 1000 according to the disclosure. In one or more examples or embodiments, the antenna system 1000 is associated with a machine learning model 1010 (e.g., in communication with a machine learning model 1010). In some examples, the machine learning model 1010 is associated with any of the antenna systems 600, 700, 800 disclosed with respect to FIGS. 6-8B. The machine learning model 1010 can be external to an autonomous vehicle 1002, such as shown in FIG. 11 , or internal to the autonomous vehicle 1002. The machine learning model 1010 is used for the determination of the antenna orientation parameter, such as discussed above, in some examples. For example, the machine learning model 1010 is used to determine an optimized angle for at least one antenna 1104 in the antenna system 1000, allowing for improved communication from the at least one antenna 1 104. In particular, a modem 1106 will typically continuously scan for the best connection for the at least one antenna 1104, resulting in higher delay, processing time, and power consumption. By using the machine learning model 1010, the output of the machine learning model 1010 can provide for a predicted best antenna position, which removes the need to continuously scan.

[141] In one or more examples or embodiments, the antenna system 1000 is for an autonomous vehicle 1002. In one or more examples or embodiments, the antenna system 1000 includes at least one antenna 1104 (multiple antennas show in FIG. 1 1 as 1 104A, 1104B, 1104C, 1104D). In one or more examples or embodiments, the antenna system 1000 includes at least one processor communicatively coupled to the at least one antenna 1 104, wherein the at least one processor is configured to execute instructions associated with a machine learning model 1010, the instructions causing the at least one processor to perform operations. In one or more examples or embodiments, the operations include obtaining location data 1108 indicative of at least one location of the autonomous vehicle 1002. In one or more examples or embodiments, the operations include inputting the location data 1108 into the machine learning model 1010. In one or more examples or embodiments, the operations include obtaining an output 1 109 from the machine learning model 1010 based on the location data 1108. In one or more examples or embodiments, the operations include determining an antenna orientation parameter indicative of a recommended position of the at least one antenna 1 104 based on the output 1109 of the machine learning model 1010, the output 1109 indicative of the antenna orientation parameter. In one or more examples or embodiments, the operations include controlling a position of the at least one antenna 1 104 based on the antenna orientation parameter.

[142] In one or more examples or embodiments, the antenna system 1000 is configured to obtain location data 1 108 and use said location data 1 108 for the determination and/or control of the position of the at least one antenna 1104. In some examples, the location data 1108 includes one or more of: a geographical location, a time of day, a weather parameter indicative of a weather condition at the location. In some examples, the location data 1108 includes GPS coordinates of the cellular towers of each carrier and/or the nearest DAS (Distributed antenna system) which will be predefined in a database. The location data 1108 can include, in addition to the vehicle GPS, one or more of: weather, time, and vehicle orientation (e.g., yaw, roll, and pitch angles). In one or more examples or embodiments, the antenna system 1000 obtains the location data 1 108 from a GPS (e.g., via a localization system such as localization system 406 of FIG. 4). In some examples, the location data 1108 is indicative of operational design domain (ODD), which is a defined geographical area where the autonomous vehicle will be operating. [143] In one or more examples or embodiments, the machine learning model 1010 includes one or more of a machine-learning system, an artificial intelligence (Al) model or system, and a convolutional neural network (CNN) (such as the convolutional neural network 420 and/or 440 discussed with respect to FIGS. 4B-4D). The particular type of model or network is not limiting. In one or more examples or embodiments, the at least one antenna 1 104 is a telecommunication antenna.

[144] In one or more examples or embodiments, the antenna system 1000 provides the location data 1108 to the machine learning model 1010. For example, the antenna system 1000 inputs the location data 1108 as an input to the machine learning model 1010. In one or more examples or embodiments, the machine learning model 1010 outputs an output 1109 based on the location data 1108. The output 1 109 can be a prediction based on the input (e.g., based on the location data 1108). In other words, the machine learning model 1010 can be configured to generate a prediction (e.g., a predicted best position of the at least one antenna 1104) based on the location data 1 108. In one or more examples or embodiments, the antenna system 1000 is configured to obtain the output 1 109 from the machine learning model 1010. In examples, the output 1 109 is indicative of an optimized position (e.g., an angle) of the at least one antenna 1104.

[145] In one or more embodiments or examples, the antenna system 1000 is configured to determine an antenna orientation parameter. For example, the antenna orientation parameter is indicative of a recommended (e.g., predicted, suggested, optimal, learned) position of the at least one antenna 1104 based on the output 1 109 of the machine learning model 1010. In one or more examples or embodiments, the antenna system 1000 is configured to control a position of the at least one antenna 1 104 based on the antenna orientation parameter. In one or more examples or embodiments, controlling the position of the at least one antenna 1104 includes controlling one or more of: an orientation of the at least one antenna 1 104, a phase of the at least one antenna 1 104, an angle of the at least one antenna 1 104, and/or a pose of the at least one antenna 1104. For example, the antenna system 1000 is configured to control a tilt of the at least one antenna 1 104, which can be advantageous for certain V2X communications. In one or more examples or embodiments, controlling the position of the at least one antenna 1104 includes controlling a rotation of the at least one antenna 1104. For example, the antenna system 1000 is configured to control the at least one antenna 1104 in the fashion disclosed above with respect to FIGS. 6-8B.

[146] In one or more examples or embodiments, the operations further includes training the machine learning model 1010 based on the location data 1108 and/or the performance parameters for one or more vehicles (discussed below). For example, the machine learning model 1010 is trained based on the location data 1108 and/or the performance parameters for one or more vehicles to identify a mapping between location data, performance parameters and antenna orientation parameter. For example, the antenna system 1000 is configured to update and/or re-train the machine learning model 1010 based on the location data 1108 and/or the performance parameters for one or more vehicles. In some examples, any collected data (such as the location data 1108 and/or the performance parameters for one or more vehicles) is used to train the machine learning model 1010. In other words, for example, the machine learning model 1010 is updated and/or retrained based on the location data 1 108 and/or the performance parameters collected for one or more vehicles, such as for vehicles of a fleet. For example, the machine learning model 1010 is updated and/or retrained based on a feedback indicative of the monitoring of the performance parameters of the antenna orientation parameter or position used. The retraining of the machine learning model may lead to improving the accuracy of the output 1109, and thereby the accuracy of the position of the antenna in a given location.

[147] In one or more examples or embodiments, the antenna system 1000 is configured to input different parameters (e.g., data, input) to the machine learning model 1010 as inputs. These different inputs can further improve the machine learning model 1010 and/or optimize the output 1109. In some examples, the output 1 109 is based on one or more of location data 1 108, performance parameter, positioning parameter, and vehicle orientation parameter as discussed herein. For example, inputs include one or more of the carriers and the network node (cell tower), with which the at least one antenna 1104 is in communication, the angle and/or orientation of the antenna of the at least one antenna 1104, the location of the autonomous vehicle 1002, the orientation of the autonomous vehicle 1002, and a quality of signals from the at least one antenna 1 104.

[148] In one or more examples or embodiments, the operations further include obtaining a performance parameter indicative of a performance (e.g., signal quality) of a communication between the at least one antenna 1104 and a network node (such as network node 906 of FIGS. 9A-9C) at the location. In one or more examples or embodiments, the operations further include inputting the performance parameter into the machine learning model 1010. In one or more examples or embodiments, the operations further include obtaining the output 1 109 from the machine learning model 1010 further based on the performance parameter. The performance parameter can be seen as the same performance parameter discussed herein. In other words, the machine learning model 1010 is configured to use the performance parameter for generating the output 1 109.

[149] In one or more examples or embodiments, the operations further include obtaining a positioning parameter indicative of a current position of the at least one antenna 1104. In one or more examples or embodiments, the operations further include inputting the positioning parameter into the machine learning model 1010. In one or more examples or embodiments, the operations further include obtaining the output 1 109 from the machine learning model 1010 further based on the positioning parameter. For example, the positioning parameter is indicative of the particular at least one antenna 1 104 position, and not the position of the autonomous vehicle 1002 itself.

[150] In one or more examples or embodiments, operations further include obtaining a vehicle orientation parameter indicative of an orientation of the autonomous vehicle 1002. In one or more examples or embodiments, operations further include inputting the vehicle orientation parameter into the machine learning model 1010. In one or more examples or embodiments, operations further include obtaining the output 1 109 from the machine learning model 1010 further based on the vehicle orientation parameter. It can be advantageous to know that the particular orientation of the autonomous vehicle 1002 when determining the optimal position for the at least one antenna 1 104.

[151] In one or more examples or embodiments, the antenna system 1000 obtains and uses data from a plurality of autonomous vehicles, such as a plurality of vehicles in a fleet (Vehicles 2-N as shown in FIG. 1 1 ). In some examples, the plurality of autonomous vehicles are operating at the same time (e.g., simultaneously). In one or more examples or embodiments, the operations further include obtaining a plurality of location data and a respective plurality of performance parameters (collectively 1 111 A, 1 111 B, 1 111 N) from a plurality of autonomous vehicles. In one or more examples or embodiments, the operations further include inputting the plurality of location data and the respective plurality of performance parameters (1 111 A, 1 111 B, 111 1 N) into the machine learning model 1010. In one or more examples or embodiments, the operations further include obtaining the output 1109 from the machine learning model 1010 further based on the plurality of location data and the respective plurality of performance parameters (1 1 11 A, 1 111 B, 1111 N). In some examples, the output 1109 from the machine learning model 1010 is indicative of a plurality of performance parameters and/or the plurality of location data (1 11 1 A, 111 1 B, 11 1 1 N). In this way, the antenna system 1000 leverages information from a number of autonomous vehicles, therefore potentially providing the most optimized position for the at least one antenna 1104. For example, the antenna system 1000 obtains an updated machine learning model which has been retrained across a fleet of vehicles, based on aggregated data from the fleet of vehicles, such as including location data, performance parameters, and antenna orientation parameters. For example, the antenna system 1000 updates and/or retrains the machine learning model based on aggregated data from the fleet of vehicles, such as including location data, performance parameters, and antenna orientation parameters.

[152] In some examples or embodiments, the antenna system 1000 is configured to train the machine learning model by collecting training data in the ODD while the modem(s) 1106 are scanning for the best connection, such as by rotating the antenna 1 104. Each GPS location, or a GPS range, would have a best connection angle (e.g., with respect to vehicle orientation and direction) identified for each cellular carrier in the training dataset. For example, modem 1 can have a best connection at GPS X1 , Y1 of 45 degrees north-east.

[153] In some examples or embodiments, the machine-learning model can be retrained. For example, the antenna system 1000 is configured to retrain the machinelearning model during service while the machine learning model is being used. For example, antenna scanning may not be used by default and the antenna angle identified by the machine learning model may not provide an acceptable performance. Accordingly, the antenna system 1000 can be configured to scan again to identify a better direction. In some examples, the antenna system 1000 identifies a new antenna direction and confirms this new direction to be better for this GPS location. In some examples, the antenna system 1000 retrains the machine-learning model with the new/modified data points.

[154] In one or more examples or embodiments, the antenna system 1000 includes a plurality of antennas 1 104A, 1104B, 1 104C, 1104D. In one or more examples or embodiments, the operations further include determining a respective antenna orientation parameter for each of the plurality of antennas 1 104A, 1 104B, 1 104C, 1 104D based on the output 1109. Alternatively, the antenna system 1000 uses the same antenna orientation parameter for all of the plurality of antennas 1 104A, 1 104B, 1 104C, 1 104D, such as when the plurality of antennas 1104A, 1104B, 1104C, 1104D are close enough together and using the same communication system (e.g., network).

[155] In one or more examples or embodiments, the antenna system 1000 includes a modem 1 106 communicatively coupled to the at least one antenna 1 104 and to the at least one processor. In one or more examples or embodiments, the modem 1 106 is configured to generate a control signal based on the antenna orientation parameter. In one or more examples or embodiments, the operations include controlling the position of the at least one antenna 1104 further based on the control signal.

[156] FIG. 12 illustrates a diagram of an example antenna system 1000 process according to the disclosure. Instead of performing a full scan of the antennas of the antenna system 1000, the modem 1 106 is configured to request (e.g., ask) the machine learning model 1010 for a recommended antenna angle of a first antenna and a second antenna. In some examples, as an input 1 114 the machine learning model 1010 obtains one or more of the particular carriers and the network node with which the antenna communicates, the location of the autonomous vehicle, and the orientation of the autonomous vehicle. In some examples, the machine learning model 1010 provides an output 1 109, such as indicative of a first antenna having a first recommended angle and a second antenna having a second recommended angle. In some examples, the modem 1 106 is configured to determine and transmit a control signal 1022 for changing the antenna orientation of the first antenna and the second antenna.

[157] As mentioned, the antenna system 600, 700, 800, 1000 may be associated with an autonomous vehicle, such as vehicle 200 shown in FIG. 2, autonomous vehicle 910 shown in FIG. 10 or autonomous vehicle 1002 shown in FIG. 1 1. In one or more embodiments or examples, the autonomous vehicle includes at least one modem. In one or more embodiments or examples, the autonomous vehicle includes at least one antenna operatively connected with the at least one modem. In one or more embodiments or examples, the autonomous vehicle is configured to determine, using at least one processor, a performance parameter indicative of a performance of a communication between the at least one antenna and a network node. In one or more embodiments or examples, the autonomous vehicle is configured to control, using the at least one processor, based on the performance parameter, a position of the at least one antenna. In one or more embodiments or examples, the autonomous vehicle is configured to control, using the at least one processor, based on the performance parameter, a connection of the at least one antenna to the at least one modem. In one or more embodiments or examples, the autonomous vehicle is configured to control, using the at least one processor, based on the performance parameter, a position of the at least one antenna and a connection of the at least one antenna to the at least one modem. In one or more examples or embodiments, the autonomous vehicle includes an antenna system. In one or more examples or embodiments, the antenna system includes at least one antenna. In one or more examples or embodiments, the antenna system includes at least one processor communicatively coupled to the at least one antenna, wherein the at least one processor is configured to execute instructions associated with a machine learning model, the instructions causing the at least one processor to perform operations. In one or more examples or embodiments, the operations include obtaining location data indicative of at least one location of the autonomous vehicle. In one or more examples or embodiments, the operations include inputting the location data into the machine learning model. In one or more examples or embodiments, the operations include obtaining an output from the machine learning model based on the location data. In one or more examples or embodiments, the operations include determining an antenna orientation parameter indicative of a recommended position of the at least one antenna based on the output of the machine learning model, the output indicative of the antenna orientation parameter. In one or more examples or embodiments, the operations include controlling a position of the at least one antenna based on the antenna orientation parameter. In one or more embodiments or examples, the autonomous vehicle is autonomous vehicle 102 of FIG. 1 or vehicle 200 of FIG. 2. The autonomous vehicle includes, for example, the antenna system 600, 700, 800, 1000.

[158] Referring now to FIG. 13, illustrated is a flowchart of a method or process 1100 for operation of a dynamic antenna system, such as for operating and/or controlling an AV. The method can be performed by a system disclosed herein, such as an AV compute 400, and a vehicle 102, 200, 910, 1002 of FIGS. 1 , 2, 3, 4, 10, 1 1 and implementations of the antenna system 600, 700, 800, 1000 of FIGS. 6-8B and FIGS. 1 1 -12. The system disclosed can include at least one processor which can be configured to carry out one or more of the operations of method 1200. The method 1200 can be performed (e.g., completely, partially, and/or the like) by another device or group of devices separate from or including system disclosed herein.

[159] In one or more embodiments or examples, the method 1200 includes determining, at step 1202, by at least one processor, a performance parameter indicative of a performance of a communication between at least one antenna and a network node. In one or more embodiments or examples, the method 1200 includes controlling, at step 1204, using the at least one processor, based on the performance parameter, a position of the at least one antenna. In one or more embodiments or examples, the method 1200 includes controlling, at step 1206, using the at least one processor, based on the performance parameter, a connection of the at least one antenna to at least one modem. In one or more embodiments or examples, the method 1200 includes controlling, at step 1204 and step 1206, using the at least one processor, based on the performance parameter, a position of the at least one antenna and a connection of the at least one antenna to at least one modem. In one or more embodiments or examples, the at least one antenna is a cellular antenna, such as an LTE and/or a 5G, or a V2X antenna. In one or more embodiments or examples, the performance parameter is indicative of SNR.

[160] In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes controlling the position of the at least one antenna relative to a position of the at least one modem. In one or more embodiments or examples, controlling the position allows the antenna to improve coverage to a known network node position.

[161] In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes controlling one or more of: an orientation of the at least one antenna, a phase of the at least one antenna, an angle of the at least one antenna, and a pose of the at least one antenna. In one or more embodiments or examples, changing the angle of the at least one antenna includes changing a tilt of the at least one antenna, which can be advantageous for V2X communication with nodes at a higher angle by improving line of sight between the at least one antenna and the network node.

[162] In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes rotating the at least one antenna. In one or more embodiments or examples, a rotation member such as rotation members 604, 704 discussed with respect to FIGS. 6, 7A-7B can be used.

[163] In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes controlling the connection of the at least one antenna to the at least one modem. In one or more embodiments or examples, controlling the connection, at step 1206, includes closing or opening a switch.

[164] In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes determining if the performance parameter satisfies a criterion. In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes, in response to determining that the performance parameter satisfies the criterion, controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem. In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes, in response to determining that the performance parameter does not satisfy the criterion, not controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem. Advantageously, the method 1200 can allow for determining if the at least one antenna is not operating properly, such as due to signal disconnection and/or degradation. The method 1200 can include scanning for the best wireless connection based on the connection, orientation, phase, etc. of the at least one antenna. Further, the method 1200 includes, for example, switching the modem to choose which antennas to be connected to.

[165] In one or more embodiments or examples, determining if the performance parameter satisfies a criterion includes determining if the performance criteria is below a performance threshold. In one or more embodiments or examples, determining if the performance parameter satisfies a criterion includes, in response to determining that the performance criteria is below the performance threshold, determining that the performance parameter satisfies the criterion. In one or more embodiments or examples, determining if the performance parameter satisfies a criterion includes, in response to determining that the performance criteria is not below the performance threshold, determining that the performance parameter does not satisfy the criterion. The performance threshold can be a general performance threshold or a comparative performance threshold.

[166] In one or more embodiments or examples, determining, at step 1202, the performance parameter includes determining a plurality of performance parameters indicative of a performance of a communication between each antenna of a plurality of antennas and a respective network node of a plurality of network nodes. In one or more embodiments or examples, each of the plurality of network nodes is associated with a different carrier operating on a different frequency band. In one or more embodiments or examples, determining, at step 1202, the performance parameter includes controlling, at step 1204 and/or step 1206, based on the respective performance parameter, a position of each of the plurality of antennas and/or a connection of each of the plurality of antennas to the at least one modem.

[167] In one or more embodiments or examples, the at least one antenna is one or more of a cellular antenna and a V2X antenna.

[168] In one or more embodiments or examples, controlling, at step 1204 and/or step 1206, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem includes operating a switch coupling the at least one antenna and the at least one modem.

[169] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details that can vary from implementation to implementation. Accordingly, the description and drawings are to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further comprising,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step/sub- entity of a previously-recited step or entity.

[170] Disclosed are non-transitory computer readable media comprising instructions stored thereon that, when executed by at least one processor, cause the at least one processor to carry out operations according to one or more of the methods disclosed herein.

[171] Also disclosed are antenna systems, autonomous vehicles, and methods according to any of the following Examples:

Example 1 . An antenna system for an autonomous vehicle, the antenna system comprising: at least one modem; and at least one antenna operatively connected with the at least one modem; wherein the antenna system is configured to: determine, using at least one processor, a performance parameter indicative of a performance of a communication between the at least one antenna and a network node; and control, using the at least one processor, based on the performance parameter, one or more of: a position of the at least one antenna, and a connection of the at least one antenna to the at least one modem.

Example 2. The antenna system of Example 1 , wherein the antenna system is configured to control the position of the at least one antenna relative to a position of the at least one modem.

Example 3. The antenna system of any one of the preceding Examples, wherein the antenna system is configured to control, using the at least one processor, based on the performance parameter, a position of the at least one antenna by controlling one or more of: an orientation of the at least one antenna, a phase of the at least one antenna, an angle of the at least one antenna, and a pose of the at least one antenna.

Example 4. The antenna system of any one of the preceding Examples, wherein the antenna system is configured to control, using the at least one processor, based on the performance parameter, the position of the at least one antenna by controlling a rotation of the at least one antenna.

Example 5. The antenna system of any one of the preceding Examples, wherein the antenna system is configured to control, using the at least one processor, based on the performance parameter, the connection of the at least one antenna to the at least one modem.

Example 6. The antenna system of any one of the preceding Examples, wherein the antenna system is configured to control the connection of the at least one antenna to the at least one modem using a switch coupled to the at least one antenna and the at least one modem. Example 7. The antenna system of any one of the preceding Examples, wherein the antenna system is configured to control, using the at least one processor, based on the performance parameter, the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem by: determining if the performance parameter satisfies a criterion; and in response to determining that the performance parameter satisfies the criterion, controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem.

Example 8. The antenna system of Example 7, wherein the performance parameter satisfies the criterion in response to the performance parameter being below a performance threshold.

Example 9. The antenna system of any one of the preceding Examples, wherein the at least one antenna is one or more of a cellular antenna and a V2X antenna.

Example 10. The antenna system of any one of the preceding Examples, wherein the antenna system comprises a plurality of antennas associated with at least two different carriers, wherein each carrier of the at least two different carriers operates on a different frequency band.

Example 11 . The antenna system of any one of the preceding Examples, wherein the antenna system comprises a plurality of modems.

Example 12. The antenna system of any one of the preceding Examples, wherein the antenna system comprises a plurality of modems and a plurality of antennas, wherein each of the plurality of antennas is connected to each of the plurality of modems via a switch.

Example 13. An autonomous vehicle comprising: at least one modem; and at least one antenna operatively connected with the at least one modem; wherein the autonomous vehicle is configured to: determine, using at least one processor, a performance parameter indicative of a performance of a communication between the at least one antenna and a network node; and control, using the at least one processor, based on the performance parameter, one or more of: a position of the at least one antenna, and a connection of the at least one antenna to the at least one modem.

Example 14. A method comprising: determining, by at least one processor, a performance parameter indicative of a performance of a communication between at least one antenna and a network node; and controlling, using the at least one processor, based on the performance parameter, one or more of: a position of the at least one antenna, and a connection of the at least one antenna to at least one modem.

Example 15. The method of Example 14, wherein controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem comprises: controlling the position of the at least one antenna relative to a position of the at least one modem.

Example 16. The method of any one of Examples 14-15, wherein controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem comprises: controlling one or more of: an orientation of the at least one antenna, a phase of the at least one antenna, an angle of the at least one antenna, and a pose of the at least one antenna.

Example 17. The method of any one of Examples 14-16, wherein controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem comprises: rotating the at least one antenna.

Example 18. The method of any one of Examples 14-17, wherein controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem comprises: controlling the connection of the at least one antenna to the at least one modem. Example 19. The method of any one of Examples 14-18, wherein controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem comprises: determining if the performance parameter satisfies a criterion; and in response to determining that the performance parameter satisfies the criterion, controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem.

Example 20. The method of Example 19, wherein determining if the performance parameter satisfies a criterion comprises: determining if the performance criteria is below a performance threshold; and in response to determining that the performance criteria is below the performance threshold, determining that the performance parameter satisfies the criterion.

Example 21. The method of any one of Examples 14-20, wherein determining the performance parameter comprises: determining a plurality of performance parameters indicative of a performance of a communication between each antenna of a plurality of antennas and a respective network node of a plurality of network nodes, wherein each of the plurality of network nodes is associated with a different carrier operating on a different frequency band; and controlling, based on the respective performance parameter, a position of each of the plurality of antennas and/or a connection of each of the plurality of antennas to the at least one modem.

Example 22. The method of any one of Examples 14-21 , wherein the at least one antenna is one or more of a cellular antenna and a V2X antenna.

Example 23. The method of any one of Examples 14-22, wherein controlling the position of the at least one antenna and/or the connection of the at least one antenna to the at least one modem comprises: operating a switch coupling the at least one antenna and the at least one modem.

Example 24. An antenna system for an autonomous vehicle, the antenna system comprising: at least one antenna; and at least one processor communicatively coupled to the at least one antenna, wherein the at least one processor is configured to execute instructions associated with a machine learning model, the instructions causing the at least one processor to perform operations comprising: obtaining location data indicative of at least one location of the autonomous vehicle; inputting the location data into the machine learning model; obtaining an output from the machine learning model based on the location data; determining an antenna orientation parameter indicative of a recommended position of the at least one antenna based on the output of the machine learning model, the output indicative of the antenna orientation parameter; and controlling a position of the at least one antenna based on the antenna orientation parameter.

Example 25. The antenna system of Example 24, wherein the operations further comprise: obtaining a performance parameter indicative of a performance of a communication between the at least one antenna and a network node at the location; inputting the performance parameter into the machine learning model; and obtaining the output from the machine learning model further based on the performance parameter.

Example 26. The antenna system of any one of Examples 24-25, wherein the operations further comprise: obtaining a positioning parameter indicative of a current position of the at least one antenna; inputting the positioning parameter into the machine learning model; and obtaining the output from the machine learning model further based on the positioning parameter. Example 27. The antenna system of any one of Examples 24-26, wherein the operations further comprise: obtaining a vehicle orientation parameter indicative of an orientation of the autonomous vehicle; inputting the vehicle orientation parameter into the machine learning model; and obtaining the output from the machine learning model further based on the vehicle orientation parameter.

Example 28. The antenna system of any of Examples 24-27, wherein the operations further comprise: obtaining a plurality of location data and a respective plurality of performance parameters from a plurality of autonomous vehicles; inputting the plurality of location data and the respective plurality of performance parameters into the machine learning model; and obtaining the output from the machine learning model further based on the plurality of location data and the respective plurality of performance parameters.

Example 29. The antenna system of any of Examples 24-28, wherein the antenna system comprises a plurality of antennas, and wherein the operations further comprise determining a respective antenna orientation parameter for each of the plurality of antennas based on the output.

Example 30. The antenna system of any of Examples 24-29, wherein the antenna system comprises a modem communicatively coupled to the at least one antenna and to the at least one processor, wherein the modem is configured to generate a control signal based on the antenna orientation parameter, and wherein the operations include controlling the position of the at least one antenna further based on the control signal.

Example 31. The antenna system of any of Examples 24-30, wherein controlling the position of the at least one antenna comprises controlling one or more of: an orientation of the at least one antenna, a phase of the at least one antenna, an angle of the at least one antenna, and a pose of the at least one antenna.

Example 32. The antenna system of any of Examples 24-31 , wherein controlling the position of the at least one antenna comprises controlling a rotation of the at least one antenna. Example 33. The antenna system of any of Examples 24-32, wherein the location data comprises one or more of: a geographical location, a time of day, a weather parameter indicative of a weather condition at the location.

Example 34. The antenna system of any of Examples 25-33, the operations further comprising training the machine learning model based on the location data and/or the performance parameters for one or more vehicles.

Example 35. An autonomous vehicle comprising: an antenna system comprising: at least one antenna; and at least one processor communicatively coupled to the at least one antenna, wherein the at least one processor is configured to execute instructions associated with a machine learning model, the instructions causing the at least one processor to perform operations comprising: obtaining location data indicative of at least one location of the autonomous vehicle; inputting the location data into the machine learning model; obtaining an output from the machine learning model based on the location data; determining an antenna orientation parameter indicative of a recommended position of the at least one antenna based on the output of the machine learning model, the output indicative of the antenna orientation parameter; and controlling a position of the at least one antenna based on the antenna orientation parameter.

Example 36. The autonomous vehicle of Example 35, wherein the operations further comprise: obtaining a performance parameter indicative of a performance of a communication between the at least one antenna and a network node at the location; inputting the performance parameter into the machine learning model; and obtaining the output from the machine learning model further based on the performance parameter.

Example 37. The autonomous vehicle of any one of Examples 35-36, wherein the operations further comprise: obtaining a positioning parameter indicative of a current position of the at least one antenna; inputting the positioning parameter into the machine learning model; and obtaining the output from the machine learning model further based on the positioning parameter.

Example 38. The autonomous vehicle of any one of Examples 35-37, wherein the operations further comprise: obtaining a vehicle orientation parameter indicative of an orientation of the autonomous vehicle; inputting the vehicle orientation parameter into the machine learning model; and obtaining the output from the machine learning model further based on the vehicle orientation parameter.

Example 39. The autonomous vehicle of any one of Examples 35-38, wherein the operations further comprise: obtaining a plurality of location data and a respective plurality of performance parameters from a plurality of autonomous vehicles; inputting the plurality of location data and the respective plurality of performance parameters into the machine learning model; and obtaining the output from the machine learning model further based on the plurality of location data and the respective plurality of performance parameters.

Example 40. The autonomous vehicle of any one of Examples 35-39, wherein the antenna system comprises a plurality of antennas, and wherein the operations further comprise determining a respective antenna orientation parameter for each of the plurality of antennas based on the output.

Example 41 . The autonomous vehicle of any one of Examples 35-40, wherein the antenna system comprises a modem communicatively coupled to the at least one antenna and to the at least one processor, wherein the modem is configured to generate a control signal based on the antenna orientation parameter, and wherein the operations include controlling the position of the at least one antenna further based on the control signal.

Example 42. The autonomous vehicle of any one of Examples 35-41 , wherein controlling the position of the at least one antenna comprises controlling one or more of: an orientation of the at least one antenna, a phase of the at least one antenna, an angle of the at least one antenna, and a pose of the at least one antenna.

Example 43. The autonomous vehicle of any one of Examples 35-42, wherein controlling the position of the at least one antenna comprises controlling a rotation of the at least one antenna.

Example 44. The autonomous vehicle of any one of Examples 35-43, wherein the location data comprises one or more of: a geographical location, a time of day, a weather parameter indicative of a weather condition at the location.

Example 45. The autonomous vehicle of any one of Examples 36-44, the operations further comprising training the machine learning model based on the location data and/or the performance parameters for one or more vehicles.

Example 46. A method of operating an antenna system, the method comprising: obtaining location data indicative of at least one location of an autonomous vehicle; inputting the location data into a machine learning model; obtaining an output from the machine learning model based on the location data; determining an antenna orientation parameter indicative of a recommended position of at least one antenna based on the output of the machine learning model, the output indicative of the antenna orientation parameter; and controlling a position of the at least one antenna based on the antenna orientation parameter.