FIELD OF THE DISCLOSURE

[0001] The present invention disclosure relates to methods, systems, storage media and apparatus to analyse the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choice in automated vehicles (AV).

BACKGROUND

[0002] EP3334624B1 granted to Baidu describes a computer-implemented method for operating an autonomous vehicle, wherein a plurality of driving scenarios is determined for a plurality of types of autonomous vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. Driving statistics of each autonomous vehicle are obtained for each of the plurality of driving scenarios of each type of autonomous vehicles. One or more driving parameters used to control and drive each autonomous vehicle of the type are recorded. A driving condition corresponding the driving scenario is captured. The slideslip caused by the recorded driving parameters and the captured driving condition are captured. However, this document does not disclose the use of a slipping-risk-related safety projection based on the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV.

[0003] EP3870489A1 pending to Waymo discloses a computer-implemented method of operating a vehicle in an autonomous driving mode to detect a traction condition. Pose information of the vehicle is obtained from sensors or perception systems. The actual pose of the vehicle on the roadway is determined and compared with an expected pose. The slippage is determined based on whether the difference exceeds a threshold. A corrective driving action is performed in response to the slippage. Alternatively, the route is re-planned. However, this document does not disclose the use of a slipping-risk-related safety projection based on the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV.

[0004] EP2583872B1 granted to Autoliv Nissin Brake Systems describes a controller of a vehicle brake. It includes a road surface friction coefficient judging module. This module is configured to judge whether a road surface on which the vehicle is running is at least a low friction coefficient road surface. The antilock braking controlling module performs a turning pressure reduction control only in the case that the road surface is judged to be the low friction coefficient road surface. However, this document does not describe a method wherein the friction coefficient is used at planning level for the trajectory planning of the electronic control unit of an automated vehicle.

[0005] US2018164827A1 pending to General Motors discloses a method for generating a vehicle path to operate an autonomous vehicle. A lateral re-entry planner system corrects lateral re-entry errors. A longitudinal re-entry planner system is used to correct a longitudinal re-entry error. Path correction commands are generated based upon the corrections provided by the lateral re-entry planner system and the longitudinal re-entry planner system. The road curvature is calculated through a cost model provided by a solver such that the vehicle slows down around a curve. However, this document does not disclose the use of a slipping-risk-related safety projection based on the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV.

[0006] EP2536606B1 granted to Conti Temic Microelectronic and Continental discloses a method for automatically preventing aquaplaning. Information relating to a section of the route and regarding the risk of aquaplaning is provided to the motor vehicle. A camera-based road sign recognition indicates aquaplaning. At least one sensor device for determining a wet roadway is provided. An assistance function is carried out to prevent aquaplaning, if there is a section of the route with a risk of aquaplaning and if a wet roadway is detected. However, this document does not describe a method wherein the friction coefficient is used at planning level for the trajectory planning of the electronic control unit of an automated vehicle.

[0007] EP3267421A1 pending to Pioneer and Increment describes a route search device comprising an acquirement unit, a route search unit and environment acquirement unit, an extraction unit and an information unit for the user. However, this document does not describe a method wherein the friction coefficient is used at planning level for the trajectory planning of the electronic control unit of an automated vehicle.

[0008] EP3875328A2 to Beijing Baidu Netcom describes a cruise control method. A driving habit weight of at least one candidate driving strategy associated with a target driving environment is acquired. The driving habit weight is determined based on historical driving data of a historical driving device of a driving user. A target driving strategy from the at least one candidate driving strategy according to the driving habit weight is selected. A cruise control on a target driving device of the driving user according to the target driving strategy is performed. However, this document does not describe a method wherein the friction coefficient is used at planning level for the trajectory planning of the electronic control unit of an automated vehicle.

[0009] US9994210B2 granted to Robert Bosch GmbH describes a method for ascertaining or evaluating a setpoint trajectory of a motor vehicle. An initial setpoint trajectory is transmitted to an evaluation unit of a vehicle control unit through a trajectory planning unit. A curve of a poser characteristic variable is received. The trajectory planning unit determines a corrected setpoint trajectory as a function of the implementable curve of the power characteristic variable. The propulsion unit is controlled based on the implementable curve of the power characteristic variable. The implementable curve of the power characteristic variable is ascertained as a function of a friction coefficient of a roadway on which the motor vehicle is traveling. However, this document does not describe a method wherein the friction coefficient is used at planning level for the trajectory planning of the electronic control unit of an automated vehicle.

[0010] US2019337509A1 to Mobileye describes a navigation system for navigating an autonomous host vehicle according to at least one navigational goal of the host vehicle. The prediction of at least one aspect of host vehicle motion is based on at least one of a determined brake pedal position, a determined throttle position, determined air resistance opposing host vehicle motion, friction, or grade of a road segment on which the host vehicle travels. The document describes the speed as the decisive information. It further discloses following main forces acting on the vehicle: friction; air resistance, gravity, brake friction and engine. The overall force is described as to include a sum of the assistive forces (e.g., throttle, acceleration, gravity when traveling down-hill, etc.) and the resistive forces (e.g., wind-resistance, friction, gravity when traveling up-hill, etc.) and a current speed of the host vehicle may be used to predict a future speed using the overall force. However, this document does not describe a method wherein the friction coefficient is used at planning level for the trajectory planning of the electronic control unit of an automated vehicle.

[0011] US2020241541A1 pending to General Motors discloses a method for piecewise sinusoid trajectory planning. Point data defining a current path plan for the ego vehicle is received, the current ego position along the current path plan is determined. The current velocity and an acceleration for the current ego position is determined. This is based in part on a velocity and acceleration derived from a previous trajectory candidates. The trajectory candidates is calculated. A graph with a grid of nodes in the velocity-time domain of the search space connected by edges is set. The graph with a shortest path algorithm is evaluated. The validity and cost with respect to desired constraints of vehicle motion and system limitations of each edge are determined by use of the piecewise sinusoidal path length, velocity, and acceleration profiles. These are parameterized to connect pairs of nodes. The optimal vehicle trajectory is set with the goal to minimize the objective cost function. The path parameters include friction p(s), grade 0(s) and curvature k(s). However, this document does not describe a method wherein the friction coefficient is used at planning level for the trajectory planning of the electronic control unit of an automated vehicle.

SUMMARY

[0012] The present inventors have surprisingly found that safer trajectories can be obtained by taking the tire-road friction coefficient already at the planning level into account for determining a safer trajectory. The method of the present invention allows to evaluate candidate trajectories according to a safety criteria taking into account the slipping risk. In one embodiment, evaluate means computing a function of tire road friction, max speed and curvature that contributes to the selection of one among multiple trajectories. Accordingly, in one embodiment, the risk of slipping is factored into the trajectory planning decision.

[0013] Accordingly, a first aspect of the invention relates to a method (200), comprising:

■ providing (202) the trajectory candidates of an electronic control unit of an automated vehicle;

■ determining (204) through the electronic control unit the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV;

■ determining (206) through the electronic control unit the slipping-risk-related safety projection of the trajectory candidates of the AV, wherein the slipping-risk-related safety projection being a function of the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV;

■ choosing (208) by the electronic control unit among the trajectory candidates the trajectory with an improved slipping-risk-related safety projection.

[0014] Another aspect of the present disclosure relates to a system for analysing the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choices in automated vehicles (AV). The system may include one or more hardware processors configured by machine-readable instructions for analysing the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choices in automated vehicles. The machine- readable instructions may be configured to obtain the trajectory candidates of an electronic control unit of an automated vehicle. The machine-readable instructions may be configured to determine through the electronic control unit the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV. The machine-readable instructions may be configured to determine through the electronic control unit the slipping-risk- related safety projection of the trajectory candidates of the AV. The machine-readable instructions may be configured to choose by the electronic control unit among the trajectory candidates the trajectory with an improved slipping-risk-related safety projection.

[0015] Another aspect of the present invention disclosure relates to a computer-readable storage medium for analysing the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choices in automated vehicles.

[0016] In some embodiments, the computer-readable storage medium may include instructions being executable by one or more processors to obtain the trajectory candidates of an electronic control unit of an automated vehicle.

[0017] In some embodiments, the computer-readable storage medium may include instructions being executable by one or more processors to determine through the electronic control unit the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV.

[0018] In some embodiments, the computer-readable storage medium may include instructions being executable by one or more processors to determine through the electronic control unit the slipping-risk-related safety projection of the trajectory candidates of the AV.

[0019] In some embodiments, the computer-readable storage medium may include instructions being executable by one or more processors to choose by the electronic control unit among the trajectory candidates the trajectory with an improved slipping-risk-related safety projection.

[0020] Another aspect of the present disclosure relates to an apparatus configured for analysing the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choice in automated vehicles.

[0021] In some aspects, the apparatus may include at least one memory storing computer program instructions and at least one processor configured to execute the computer program instructions to cause the apparatus at least to perform operations associated with analysing the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choice in automated vehicles.

[0022] In some aspects, the computer program instructions may include obtaining the trajectory candidates of an electronic control unit of an automated vehicle. [0023] In some aspects, the computer program instructions may include determining through the electronic control unit the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV.

[0024] In some aspects, the computer program instructions may include determining through the electronic control unit the slipping-risk-related safety projection of the trajectory candidates of the AV.

[0025] In some aspects, the computer program instructions may include choosing by the electronic control unit among the trajectory candidates the trajectory with an improved slipping- risk-related safety projection.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] FIG. 1 illustrates a system configured for analysing the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choices in automated vehicles.

[0027] FIG. 2 illustrates a method for analysing the tire-road friction estimation of trajectory candidates at planner level for safer trajectory choices in automated vehicles.

DETAILED DESCRIPTION

[0028] FIG. 1 illustrates a system configured for safer trajectory candidate choice, in accordance with one or more embodiments. In some cases, system 100 may include one or more computing platforms 102. The one or more remote computing platforms 102 may be communicably coupled with one or more remote platforms 104. In some cases, users may access the system 100 via remote platform(s) 104.

[0029] The one or more computing platforms 102 may be configured by machine-readable instructions 106. Machine-readable instructions 106 may include modules. The modules may be implemented as one or more of functional logic, hardware logic, electronic circuitry, software modules, and the like. The modules may include one or more of trajectory candidates providing module 108, determining module 110, safety projection determining module 112, trajectory choosing module 114, and/or other modules.

[0030] Trajectory candidates providing module 108 may be configured to provide the trajectory candidates of an electronic control unit of an automated vehicle. Determining module 110 may be configured to determine through the electronic control unit the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV. Safety projection determining module 112 may be configured to determine through the electronic control unit the slipping-risk-related safety projection of the trajectory candidates of the AV. The slipping-risk-related safety projection being a function of the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV. T rajectory choosing module 114 may be configured to choose by the electronic control unit among the trajectory candidates the trajectory with an improved slipping-risk-related safety projection.

[0031] In some cases, the one or more computing platforms 102, may be communicatively coupled to the remote platform(s) 104. In some cases, the communicative coupling may include communicative coupling through a networked environment 116. The networked environment 116 may be a radio access network, such as LTE or 5G, a local area network (LAN), a wide area network (WAN) such as the Internet, or wireless l_AN (WLAN), for example. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which one or more computing platforms 102 and remote platform(s) 104 may be operatively linked via some other communication coupling. The one or more one or more computing platforms 102 may be configured to communicate with the networked environment 116 via wireless or wired connections. In addition, in an embodiment, the one or more computing platforms 102 may be configured to communicate directly with each other via wireless or wired connections. Examples of one or more computing platforms 102 may include, but is not limited to, smartphones, wearable devices, tablets, laptop computers, desktop computers, Internet of Things (loT) device, or other mobile or stationary devices. In an embodiment, system 100 may also include one or more hosts or servers, such as the one or more remote platforms 104 connected to the networked environment 116 through wireless or wired connections. According to one embodiment, remote platforms 104 may be implemented in or function as base stations (which may also be referred to as Node Bs or evolved Node Bs (eNBs)). In other embodiments, remote platforms 104 may include web servers, mail servers, application servers, etc. According to certain embodiments, remote platforms 104 may be standalone servers, networked servers, or an array of servers.

[0032] In some cases, the relationship between the trajectory curvature and the maximum speed the AV can follow without skidding is:

[0033] where

[0034] p is tire-road friction coefficient.

[0035] g is gravitation acceleration, 9.81 m/s ^A 2. [0036] R is the radius of the curvature of the trajectory candidates.

[0037] In some cases, the current trajectory is used to extract the radius of curvature R.

[0038] In some cases, the current tire-road friction coefficient relates to road and tire conditions to determine the friction coefficient. The tire-road friction coefficient may be obtained from a predefined table, a function, or calculated online.

[0039] In some cases, the tire-road friction coefficient may be obtained from a pre-defined table, a function, or calculated online. For example, the tire-road friction coefficient may be read from a table or determined. The entry information may be the current detected weather condition, the road condition, the vehicle condition, the tire model, the tire state, or the possible trajectory generated or combinations thereof.

[0040] In some cases, the method of the invention further comprises executing by the electronic control unit the trajectory with the best slipping-risk-related safety projection, the electronic control unit of the AV performs safety actions based on the slipping-risk-related safety projection of the chosen trajectory and if the planner choses a trajectory with an unsafe slipping-risk-related safety projection a notification may be sent to the control unit.

[0041] In some cases, if the planner choses a trajectory with an unsafe slipping-risk-related safety projection, the control unit executes an action that reduces the risk associated with the unsafe slipping-risk-related safety projection, if the planner choses a trajectory with an unsafe slipping- risk-related safety projection, the control unit executes a break action in order to reduce the risk associated with the unsafe slipping-risk-related safety projection and if the planner choses a trajectory with an unsafe slipping-risk-related safety projection, the control unit executes a reduction of acceleration in order to reduce the risk associated with the unsafe slipping-risk- related safety projection.

[0042] In some cases, the tire-road coefficient may be calculated as a function of the road surface condition, the weather condition and the vehicle configuration, the vehicle configuration comprises one or more of the load weight of the AV, the tyre type or the tyre state, or a combination thereof and the tire-road coefficient may be obtained from a predefined table, a function or calculated online.

[0043] In some cases, the tire-road coefficient may be 0.7 for dry roads and the tire-road coefficient may be 0.4 for wet roads for standard vehicles. [0044] In some cases, the one or more computing platforms 102, may be communicatively coupled to the remote platform(s) 104.

[0045] In some cases, the communicative coupling may include communicative coupling through a networked environment 116. The networked environment 116 may be a radio access network, such as LTE or 5G, a local area network (LAN), a wide area network (WAN) such as the Internet, or wireless LAN (WLAN), for example.lt will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which one or more computing platforms 102 and remote platform(s) 104 may be operatively linked via some other communication coupling. The one or more one or more computing platforms 102 may be configured to communicate with the networked environment 116 via wireless or wired connections. In addition, in an embodiment, the one or more computing platforms 102 may be configured to communicate directly with each other via wireless or wired connections. Examples of one or more computing platforms 102 may include, but is not limited to, smartphones, wearable devices, tablets, laptop computers, desktop computers, Internet of Things (loT) device, or other mobile or stationary devices.

[0046] In an embodiment, system 100 may also include one or more hosts or servers, such as the one or more remote platforms 104 connected to the networked environment 116 through wireless or wired connections.

[0047] According to one embodiment, remote platforms 104 may be implemented in or function as base stations (which may also be referred to as Node Bs or evolved Node Bs (eNBs)).

[0048] In other embodiments, remote platforms 104 may include web servers, mail servers, application servers, etc.

[0049] According to certain embodiments, remote platforms 104 may be standalone servers, networked servers, or an array of servers.

[0050] The one or more computing platforms 102 may include one or more processors 122 for processing information and executing instructions or operations. One or more processors 122 may be any type of general or specific purpose processor. In some cases, multiple processors 122 may be utilized according to other embodiments. In fact, the one or more processors 122 may include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and processors based on a multi-core processor architecture, as examples. In some cases, the one or more processors 122 may be remote from the one or more computing platforms 102, such as disposed within a remote platform like the one or more remote platforms 122 of Fig. 1.

[0051] The one or more processors 122 may perform functions associated with the operation of system 100 which may include, for example, precoding of antenna gain/phase parameters, encoding and decoding of individual bits forming a communication message, formatting of information, and overall control of the one or more computing platforms 102, including processes related to management of communication resources.

[0052] The one or more computing platforms 102 may further include or be coupled to a memory 116 (internal or external), which may be coupled to one or more processors 122, for storing information and instructions that may be executed by one or more processors 122. Memory 116 may be one or more memories and of any type suitable to the local application environment, and may be implemented using any suitable volatile or nonvolatile data storage technology such as a semiconductor-based memory device, a magnetic memory device and system, an optical memory device and system, fixed memory, and removable memory. For example, memory 116 can consist of any combination of random access memory (RAM), read only memory (ROM), static storage such as a magnetic or optical disk, hard disk drive (HDD), or any other type of non-transitory machine or computer readable media. The instructions stored in memory 116 may include program instructions or computer program code that, when executed by one or more processors 122, enable the one or more computing platforms 102 to perform tasks as described herein.

[0053] In some embodiments, one or more computing platforms 102 may also include or be coupled to one or more antennas for transmitting and receiving signals and/or data to and from one or more computing platforms 102. The one or more antennas may be configured to communicate via, for example, a plurality of radio interfaces that may be coupled to the one or more antennas. The radio interfaces may correspond to a plurality of radio access technologies including one or more of LTE, 5G, WLAN, Bluetooth, near field communication (NFC), radio frequency identifier (RFID), ultrawideband (UWB), and the like. The radio interface may include components, such as filters, converters (for example, digital-to-analog converters and the like), mappers, a Fast Fourier Transform (FFT) module, and the like, to generate symbols for a transmission via one or more downlinks and to receive symbols (for example, via an uplink).

[0054] Fig. 2 illustrate an example flow diagram of a method 200, according to one embodiment. The method 200 may include providing the trajectory candidates of an electronic control unit of an automated vehicle at block 202. The method 200 may include determining through the electronic control unit the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV at block 204. The method 200 may include determining through the electronic control unit the slipping-risk-related safety projection of the trajectory candidates of the AV at block 206, the slipping-risk-related safety projection being a function of the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV. The method 200 may include choosing by the electronic control unit among the trajectory candidates the trajectory with an improved slipping- risk-related safety projection at block 208.

[0055] In some cases, the method 200 may be performed by one or more hardware processors, such as the processors 118 of Fig. 1 , configured by machine-readable instructions, such as the machine readable instructions 106 of Fig. 1. In this aspect, the method 200 may be configured to be implemented by the modules, such as the modules 108, 110, 112 and/or 114 discussed above in Fig. 1.

EXAMPLE

[0056] Example 1 includes a method comprising: obtaining the trajectory candidates of an electronic control unit of an automated vehicle, determining through the electronic control unit the tire-road friction coefficient, the maximum drivable speed and the maximum curvature of the trajectory candidates of the AV, determining through the electronic control unit the slipping-risk- related safety projection of the trajectory candidates of the AV, and choosing by the electronic control unit the trajectory with an improved slipping-risk-related safety projection.

[0057] Example 2 includes the method of example(s) 1 and/or some other example(s) herein, wherein the method further comprises executing by the electronic control unit the trajectory with the best slipping-risk-related safety projection.

[0058] Example 3 includes the method of example(s) 1 and/or some other example(s) herein, wherein the electronic control unit of the AV performs safety actions based on the slipping-risk- related safety projection of the chosen trajectory.

[0059] Example 4 includes the method of example(s) 1 and/or some other example(s) herein, wherein if the planner choses a trajectory with an unsafe slipping-risk-related safety projection a notification is sent to the control unit.

[0060] Example 5 includes the method of example(s) 1 and/or some other example(s) herein, wherein if the planner choses a trajectory with an unsafe slipping-risk-related safety projection, the control unit executes an action that reduces the risk associated with the unsafe slipping-risk- related safety projection. [0061] Example 6 includes the method of example(s) 1 and/or some other example(s) herein, wherein if the planner choses a trajectory with an unsafe slipping-risk-related safety projection, the control unit executes a break action in order to reduce the risk associated with the unsafe slipping-risk-related safety projection.

[0062] Example 7 includes the method of example(s) 1 and/or some other example(s) herein, wherein if the planner choses a trajectory with an unsafe slipping-risk-related safety projection, the control unit executes a reduction of acceleration in order to reduce the risk associated with the unsafe slipping-risk-related safety projection.

[0063] Example 8 includes the method of example(s) 1 and/or some other example(s) herein, wherein the tire-road coefficient is calculated as a function of the road surface condition, the weather condition and the vehicle configuration.

[0064] Example 9 includes the method of example(s) 1 and/or some other example(s) herein, wherein the vehicle configuration comprises one or more of the load weight of the AV, the tyre type or the tyre state, or a combination thereof.

[0065] Example 10 includes the method of example(s) 1 and/or some other example(s) herein, wherein the tire-road coefficient is obtained from a predefined table, a function or calculated online.