Method for route planning for a vehicle

Abstract

Method (100) for route planning for a vehicle (12), the method (100) comprising: Determining (104) a predicted trajectory (34) of a remote vehicle (32), wherein the predicted trajectory (34) of the remote vehicle (32) comprises a plurality of nodes (36) of the predicted trajectory; Determining (106) a plurality of possible trajectories (38) for the vehicle (12), wherein the plurality of possible trajectories (38) comprises a plurality of nodes (40) of possible trajectories; Determining (108, 110, 112) one or more evaluation metrics of each of the plurality of nodes (40) of possible trajectories based at least in part on a weather condition in an environment around the vehicle (12); Selecting (114) an optimal trajectory for the vehicle (12) from the plurality of possible trajectories (38) based at least in part on the one or more evaluation metrics of each of the plurality of nodes (40) of possible trajectories; and performing (116) a first action based at least partially on the optimal trajectory, wherein the one or more evaluation metrics further comprise a collision potential score, and wherein determining (108) the collision potential score for each of the plurality of nodes (40) of possible trajectories further comprises: Determining (502) a plurality of uncertainties (50), wherein the plurality of uncertainties (50) comprises a plurality of uncertainties (50a) of the predicted trajectory and a plurality of uncertainties (50b) of possible trajectories, wherein each of the plurality of uncertainties (50a) of the predicted trajectory corresponds to one of the plurality of nodes (36) of the predicted trajectory and wherein each of the plurality of uncertainties (50b) of possible trajectories corresponds to one of the plurality of nodes (40) of possible trajectories; Determining (508) a plurality of biased uncertainties (60a) of the predicted trajectory and a plurality of biased uncertainties (60b) of possible trajectories by applying a bias to each of the plurality of uncertainties (50a) of the predicted trajectory and each of the plurality of uncertainties (50b) of possible trajectories, wherein the bias is based at least in part on the weather condition in the environment around the vehicle (12); and Determining (510) the collision potential score for each of the plurality of nodes (40) of possible trajectories based at least in part on the plurality of biased uncertainties (60a) of the predicted trajectory and the plurality of biased uncertainties (60b) of possible trajectories, wherein the one or more evaluation metrics further comprise a maneuverability score, and wherein determining (110) the maneuverability score for each of the plurality of nodes (40) of possible trajectories further comprises: Determining (704) an estimated drive system torque required to reach each of the plurality of nodes (40) of possible trajectories; and Determining (706) the maneuverability score for each of the plurality of nodes (40) of possible trajectories based at least in part on the estimated propulsion system torque using a formula: mi = T i TMAX where m i is the maneuverability score for an i-th node of the plurality of nodes (40) of possible trajectories, T i is the estimated drive system torque required to reach the i-th node of the plurality of nodes (40) of possible trajectories, and TMAX is a maximum torque available from a drive system of the vehicle (12), wherein the one or more evaluation metrics further comprise a traffic violation score, and wherein the traffic violation score quantifies a legality of maneuvering the vehicle (12) to each of the plurality of nodes (40) of possible trajectories, wherein selecting (114) the optimal trajectory further comprises: Selecting a subset of the plurality of nodes (40) of possible trajectories as the optimal trajectory, wherein the subset of the plurality of nodes (40) of possible trajectories is selected to minimize an objective function: c = ∑ i = 0 NN − i N ∗ ( pi + vi + mi ) where c is the objective function, N is a set of nodes in the subset of the plurality of nodes (40) of possible trajectories, p iis the collision potential score of an i-th node of the subset of the plurality of nodes (40) of possible trajectories, υ i is the traffic violation score of the i-th node of the subset of the plurality of nodes (40) of possible trajectories and m i is the maneuverability score of the i-th node of the subset of the plurality of nodes (40) of possible trajectories, wherein each of the subset of the plurality of nodes (40) of possible trajectories originates from a same one of the plurality of possible trajectories (38), and wherein a first node of the subset of the plurality of nodes (40) of possible trajectories and a second node of the subset of the plurality of nodes (40) of possible trajectories have a collision potential score of less than one.

Description

IntroductionThe present disclosure relates to advanced driver assistance and automated driving systems and methods for vehicles, and more particularly to vehicle path planning systems and methods.The document DE 10 2013 016 488 A1 discloses a method for controlling a motor vehicle, in which a switch is temporarily made to an operating mode of the vehicle, in which the driver cannot intervene in the operation of the vehicle if a triggering condition is fulfilled. The publications DE 10 2016 003 026 A1, DE 10 2022 124 517 A1, DE 10 2004 009 515 A1 and DE 600 16 815 T2 disclose related methods.To increase occupant awareness and comfort, vehicles may be equipped with advanced driver assistance systems (ADAS) and / or automated driving systems (ADS). ADAS systems may utilize various sensors such as cameras, radar, and LiDAR (Light Detection and Ranging) to detect and identify objects near the vehicle that include other vehicles, pedestrians, road configurations, traffic signs, and road markings. ADAS systems may take actions based on environmental conditions around the vehicle, such as applying brakes or alerting an occupant of the vehicle. ADS systems may utilize various sensors to detect objects in the environment around the vehicle and control the vehicle to navigate the vehicle through the environment to a predetermined destination. However, current ADAS and ADAS systems may not account for adverse weather conditions, which may result in non-ideal path planning and / or non-ideal predictions of vehicle trajectories. Moreover, current ADAS and ADAS systems may not take into account non-conventional maneuvers that may be necessary to avoid collision in adverse weather conditions.Thus, although ADAS and ADAS systems and methods accomplish their intended purpose, it is an object of the invention to provide a new and improved method of planning a path for a vehicle.SummaryThe object is achieved by the features of claim 1. Advantageous further developments are evident from the dependent claims.According to several aspects, a method for planning a path for a vehicle is provided. The method may include determining a predicted trajectory of a remote vehicle. The predicted trajectory of the remote vehicle includes a plurality of nodes of the predicted trajectory. The method may further include determining a plurality of possible trajectories for the vehicle. The plurality of possible trajectories includes a plurality of nodes of possible trajectories. The method may further include determining one or more evaluation metrics of each of the plurality of nodes of possible trajectories based at least in part on a weather condition in an environment around the vehicle. The method may further include selecting an optimal trajectory for the vehicle from the plurality of possible trajectories based at least in part on the one or more evaluation metrics of each of the plurality of nodes of possible trajectories. The method may further include performing a first action based at least in part on the optimal trajectory.In another aspect of the present disclosure, the one or more evaluation metrics further include a collision potential score. Determining the collision potential score for each of the plurality of nodes of possible trajectories may further include determining a plurality of uncertainties. The plurality of uncertainties comprises a plurality of uncertainties of the predicted trajectory and a plurality of uncertainties of possible trajectories. Each of the plurality of uncertainties of the predicted trajectory corresponds to one of the plurality of nodes of the predicted trajectory. Each of the plurality of possible trajectory uncertainties corresponds to one of the plurality of possible trajectory nodes. Determining the collision potential score for each of the plurality of nodes of possible trajectories may further include determining a plurality of distorted uncertainties of the predicted trajectory and a plurality of distorted uncertainties of possible trajectories by applying a bias (bias) to each of the plurality of uncertainties of the predicted trajectory and each of the plurality of uncertainties of possible trajectories. The distortion is based at least in part on the weather condition in the environment around the vehicle. Determining the collision potential score for each of the plurality of nodes of possible trajectories may further include determining the collision potential score for each of the plurality of nodes of possible trajectories based at least in part on the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories.In another aspect of the present disclosure, determining the plurality of uncertainties may further comprise determining the plurality of uncertainties based on at least one of: the weather condition in the environment around the vehicle and a location error of one or more of a plurality of vehicle sensors.In another aspect of the present disclosure, determining the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories may further comprise determining a longitudinal distortion for each of the plurality of nodes of possible trajectories and each of the plurality of nodes of the predicted trajectory. Determining the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories may further comprise determining a lateral distortion for each of the plurality of nodes of possible trajectories and each of the plurality of nodes of the predicted trajectory. The lateral distortion is determined based at least in part on a dynamic-based model and the weather condition in the environment around the vehicle. Determining the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories may further comprise applying the longitudinal distortion and the lateral distortion to each of the plurality of uncertainties of the predicted trajectory. Determining the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories may further comprise applying the longitudinal distortion and the lateral distortion to each of the plurality of uncertainties of possible trajectories.In another aspect of the present disclosure, determining the collision potential score for each of the plurality of nodes of possible trajectories may further comprise determining the collision potential score using a formula: wherein p i is the collision potential score for an i-th node of the plurality of nodes of possible trajectories, r i is one of the plurality of distorted uncertainties of the predicted trajectory corresponding to an i-th node of the plurality of nodes of the predicted trajectory, e i is one of the plurality of distorted uncertainties of possible trajectories corresponding to the i-th node of the plurality of nodes of possible trajectories, n is an intersection operator, and U is a merging operator.In another aspect of the present disclosure, the one or more evaluation metrics further comprise a maneuverability score. Determining the maneuverability score for each of the plurality of nodes of possible trajectories may further comprise determining an estimated propulsion system torque required to reach each of the plurality of nodes of possible trajectories. Determining the maneuverability score for each of the plurality of nodes of possible trajectories may further comprise determining the maneuverability score for each of the plurality of nodes of possible trajectories based at least in part on the estimated propulsion system torque using a formula: wherein m i is the maneuverability score for the i-th node of the plurality of nodes of possible trajectories, T i is the estimated propulsion system torque required to reach the i-th node of the plurality of nodes of possible trajectories, and T MAX is a maximum torque available from a propulsion system of the vehicle.In another aspect of the present disclosure, the one or more evaluation metrics also include a traffic violation score. The traffic violation score quantitates legibility of maneuvering the vehicle to each of the plurality of nodes of possible trajectories.In another aspect of the present disclosure, selecting the optimal trajectory may further include selecting a subset of the plurality of nodes of possible trajectories as the optimal trajectory. The subset of the plurality of nodes of possible trajectories is selected to minimize a sum of each of the one or more evaluation metrics of each of the subset of the plurality of nodes of possible trajectories.In another aspect of the present disclosure, selecting the optimal trajectory may further include selecting a subset of the plurality of nodes of possible trajectories as the optimal trajectory. The subset of the plurality of nodes of possible trajectories is selected to minimize a target function: where c is the target function, N is a set of nodes in the subset of the plurality of nodes of possible trajectories, p is i the collision potential score of an i-th node of the subset of the plurality of nodes of possible trajectories, v is i the traffic violation score of the i-th node of the subset of the plurality of nodes of possible trajectories, and m is i the maneuverability score of the i-th node of the subset of the plurality of nodes of possible trajectories. Each of the subset of the plurality of nodes of possible trajectories originates from a same one of the plurality of possible trajectories. A first node of the subset of the plurality of possible trajectory nodes and a second node of the subset of the plurality of possible trajectory nodes have a collision potential score of less than one.In another aspect of the present disclosure, performing the first action may further include adjusting an operation of an automated driving system of the vehicle based at least in part on the optimal trajectory.According to several aspects, a system for planning a path for a vehicle is provided. The system may include a plurality of vehicle sensors, an automated driving system, and a controller in electrical communication with the plurality of vehicle sensors and the automated driving system. The controller is programmed to determine a predicted trajectory of a remote vehicle using the plurality of vehicle sensors. The predicted trajectory of the remote vehicle includes a plurality of nodes of the predicted trajectory. The controller is further programmed to determine a plurality of possible trajectories for the vehicle using the plurality of vehicle sensors. The plurality of possible trajectories includes a plurality of nodes of possible trajectories. The controller is further programmed to determine one or more evaluation metrics of each of the plurality of nodes of possible trajectories based at least in part on a weather condition in an environment around the vehicle. The weather condition is determined using the plurality of vehicle sensors. The controller is further programmed to select, from the plurality of possible trajectories, an optimal trajectory for the vehicle based at least in part on the one or more evaluation metrics of each of the plurality of nodes of possible trajectories. The controller is further programmed to adjust an operation of the automated driving system based at least in part on the optimal trajectory.In another aspect of the present disclosure, the one or more evaluation metrics further comprise a collision potential score. The controller is further programmed to determine a plurality of uncertainties The plurality of uncertainties comprises a plurality of uncertainties of the predicted trajectory and a plurality of uncertainties of possible trajectories. Each of the plurality of uncertainties of the predicted trajectory corresponds to one of the plurality of nodes of the predicted trajectory. Each of the plurality of possible trajectory uncertainties corresponds to one of the plurality of possible trajectory nodes. To determine the collision potential score for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine a plurality of distorted uncertainties of the predicted trajectory and a plurality of distorted uncertainties of possible trajectories by applying a distortion to each of the plurality of uncertainties of the predicted trajectory and each of the plurality of uncertainties of possible trajectories. The distortion is based at least in part on the weather condition in the environment around the vehicle. To determine the collision potential score for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine the collision potential score for each of the plurality of nodes of possible trajectories based at least in part on the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories.In another aspect of the present disclosure, to determine the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories, the controller is further programmed to determine a longitudinal distortion for each of the plurality of nodes of possible trajectories and each of the plurality of nodes of the predicted trajectory. To determine the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories, the controller is further programmed to determine a lateral distortion for each of the plurality of nodes of possible trajectories and each of the plurality of nodes of the predicted trajectory. The lateral distortion is determined based at least in part on a dynamic-based model and the weather condition in the environment around the vehicle. To determine the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories, the controller is further programmed to apply the longitudinal distortion and the lateral distortion to each of the plurality of uncertainties of the predicted trajectory. To determine the plurality of distorted uncertainties of the predicted trajectory and the plurality of distorted uncertainties of possible trajectories, the controller is further programmed to apply the longitudinal distortion and the lateral distortion to each of the plurality of uncertainties of possible trajectories.In another aspect of the present disclosure, to determine the collision potential score for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine the collision potential score using a formula: wherein p i is the collision potential score for an i-th node of the plurality of nodes of possible trajectories, r i is one of the plurality of distorted uncertainties of the predicted trajectory corresponding to an i-th node of the plurality of nodes of the predicted trajectory, e i is one of the plurality of distorted uncertainties of possible trajectories corresponding to the i-th node of the plurality of nodes of possible trajectories, n is an intersection operator, and U is an association operator.In another aspect of the present disclosure, the one or more evaluation metrics further comprise a maneuverability score. To determine the maneuverability score for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine an estimated propulsion system torque required to reach each of the plurality of nodes of possible trajectories. To determine the maneuverability score for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine the maneuverability score for each of the plurality of nodes of possible trajectories based at least in part on the estimated propulsion system torque using a formula: wherein m i is the maneuverability score for the i-th node of the plurality of nodes of possible trajectories, T i is the estimated propulsion system torque required to reach the i-th node of the plurality of nodes of possible trajectories, and T MAX is a maximum torque available from a propulsion system of the vehicle.In another aspect of the present disclosure, the one or more evaluation metrics further include a traffic violation score. The traffic violation score quantitates the legibility of maneuvering the vehicle to each of the plurality of nodes of possible trajectories.In another aspect of the present disclosure, to select the optimal trajectory, the controller is further programmed to select a subset of the plurality of nodes of possible trajectories as the optimal trajectory. The subset of the plurality of nodes of possible trajectories is selected to minimize a target function: where c is the target function, N is a set of nodes in the subset of the plurality of nodes of possible trajectories, p is i the collision potential score of an i-th node of the subset of the plurality of nodes of possible trajectories, v is i the traffic violation score of the i-th node in the subset of the plurality of nodes of possible trajectories, and m is i the maneuverability score of the i-th node of the subset of the plurality of nodes of possible trajectories. Each of the subset of the plurality of nodes of possible trajectories originates from a same one of the plurality of possible trajectories. A first node of the subset of the plurality of possible trajectory nodes and a second node of the subset of the plurality of possible trajectory nodes have a collision potential score of less than one.According to several aspects, a system for planning a path for a vehicle is provided. The system may include a plurality of vehicle sensors, an automated driving system, and a controller in electrical communication with the plurality of vehicle sensors and the automated driving system. The controller is programmed to determine a predicted trajectory of a remote vehicle using the plurality of vehicle sensors. The predicted trajectory of the remote vehicle includes a plurality of nodes of the predicted trajectory. The controller is further programmed to determine a plurality of possible trajectories for the vehicle using the plurality of vehicle sensors. The plurality of possible trajectories includes a plurality of nodes of possible trajectories. The controller is further programmed to perform a closure check (proximity check) of the plurality of possible trajectories. The proximity check includes removing a first trajectory from the plurality of possible trajectories in response to determining that the first trajectory includes one or more of the plurality of nodes of possible trajectories that are within a predetermined minimum stopping distance of one or more of the plurality of nodes of the predicted trajectory. The controller is further programmed to determine one or more evaluation metrics of each of the plurality of nodes of possible trajectories based at least in part on a weather condition in an environment around the vehicle. The one or more evaluation metrics include a collision potential score, a maneuverability score, and a traffic violation score. The controller is further programmed to select, from the plurality of possible trajectories, an optimal trajectory for the vehicle based at least in part on the one or more evaluation metrics of each of the plurality of nodes of possible trajectories. The controller is further programmed to adjust operation of the automated driving system. Operation of the automated driving system is adjusted so that the vehicle exits a lane boundary based at least in part on the optimal trajectory.In another aspect of the present disclosure, to determine the collision potential for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine a plurality of uncertainties. The plurality of uncertainties comprises a plurality of uncertainties of the predicted trajectory and a plurality of uncertainties of possible trajectories. Each of the plurality of uncertainties of the predicted trajectory corresponds to one of the plurality of nodes of the predicted trajectory. Each of the plurality of possible trajectory uncertainties corresponds to one of the plurality of possible trajectory nodes. To determine the collision potential score for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine a plurality of distorted uncertainties of the predicted trajectory and a plurality of distorted uncertainties of possible trajectories by applying a distortion to each of the plurality of uncertainties of the predicted trajectory and each of the plurality of uncertainties of possible trajectories. The distortion is based at least in part on the weather condition in the environment around the vehicle. To determine the collision potential score for each of the plurality of nodes of possible trajectories, the controller is further programmed to determine the collision potential score for each of the plurality of nodes of possible trajectories based at least in part on the plurality of distorted uncertainties of the predicted trajectory and a plurality of distorted uncertainties of possible trajectories.In another aspect of the present disclosure, to select the optimal trajectory, the controller is further programmed to select a subset of the plurality of nodes of possible trajectories as the optimal trajectory. The subset of the plurality of nodes of possible trajectories is selected to minimize a target function: where c is the target function, N is a set of nodes in the subset of the plurality of nodes of possible trajectories, p is i the collision potential score of an i-th node of the subset of the plurality of nodes of possible trajectories, v is i the traffic violation score of the i-th node of the subset of the plurality of nodes of possible trajectories, and m is i the maneuverability score of the i-th node of the subset of the plurality of nodes of possible trajectories. Each of the subset of the plurality of nodes of possible trajectories originates from a same one of the plurality of possible trajectories. A first node of the subset of the plurality of possible trajectory nodes and a second node of the subset of the plurality of possible trajectory nodes have a collision potential score of less than one.Further areas of applicability will become apparent from the description provided herein. It should be understood that the specification and specific examples are for illustrative purposes only.Brief Description of the DrawingsThe drawings described herein are for illustrative purposes only. FIG. 1 is a schematic illustration of a system for planning a path for a vehicle according to an exemplary embodiment; FIG. 2 is a schematic illustration of a first example road scene according to an example embodiment; FIG. 3 is a flow diagram of a method for planning a path for a vehicle according to an example embodiment; FIG. 4 is a schematic illustration of a second example road scene including uncertainties according to an example embodiment; FIG. 5 is a flow diagram of a method for determining a collision potential score according to an example embodiment; FIG. 6 is a schematic illustration of a third example road scene including distorted uncertainties according to an example embodiment; and FIG. 7 is a flow diagram of a method for determining maneuverability score, according to an example embodiment.Detailed DescriptionThe following description is merely exemplary in nature.Adverse weather conditions such as extremely high or low temperatures, precipitation, and / or the like may result in adverse road surface conditions such as wet, icy, and / or snowy road surface conditions. Vehicle dynamics may be affected by the foregoing weather and / or road surface conditions, resulting in altered driving behavior characteristics, driving characteristics, altered vehicle performance, and / or the like. Moreover, adverse weather and / or road surface conditions may present scenarios that require avoidance or other non-conventional action to avoid a collision. Therefore, the present disclosure provides a new and improved system and method for planning a path for a vehicle that takes into account the effects of weather and road surface conditions on vehicle dynamics and contemplates the use of non-conventional actions to avoid collisions at extreme weather conditions.Referring to FIG. 1, a system for planning a path for a vehicle is illustrated and generally designated by reference numeral 10. The system 10 is shown with an example vehicle 12. Although a passenger car is illustrated, the vehicle 12 may be any type of vehicle without departing from the scope of the present disclosure. The system 10 generally includes a controller 14, a plurality of vehicle sensors 16, and an automated driving system 18.The controller 14 is used to implement a method 100 for path planning for a vehicle, as described below. The controller 14 includes at least one processor 20 and a non-transitory computer readable storage device or media 22, the processor 20 may be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among multiple processors connected to the controller 14, a semiconductor-based microprocessor (in the form of a microchip or chip set), a macroprocessor, a combination thereof, or generally an apparatus for executing instructions. The computer readable storage device or media 22 may include, for example, volatile and non-volatile memory in read only memory (ROM), random access memory (RAM), and keep alive memory (KAM). KAM is persistent or non-volatile memory that can be used to store various operating variables while the processor 20 is powered down. The computer readable storage device or media 22 may be implemented using a number of storage devices, such as programmable read only memory (PROMs), erasable programmable read only memory (EPROMs), electrically erasable programmable read only memory (EEPROMs), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which represent executable instructions, used by the controller 14 to control various systems of the vehicle 12. The controller 14 may also be comprised of a plurality of controllers in electrical communication with one another. The controller 14 may be connected to additional systems and / or controllers of the vehicle 12, allowing the controller 14 to access data such as speed, acceleration, braking, and steering angle of the vehicle 12.The controller 14 is in electrical communication with the plurality of vehicle sensors 16 and the automated driving system 18. In an exemplary embodiment, the electrical communication is configured using, for example, a CAN network, a FLEXRAY network, a local area network (e.g., WiFi, Ethernet, and the like), a serial peripheral interface network (SPI), or the like. It should be understood that various, additional wired and wireless techniques and communication protocols for communicating with the controller 14 are within the scope of the present disclosure.The plurality of vehicle sensors 16 are used to acquire information relevant to the vehicle 12. In an exemplary embodiment, the plurality of vehicle sensors 16 includes a camera system 24, a vehicle communication system 26, and a global navigation satellite system (GNSS) 28.In another exemplary embodiment, the plurality of vehicle sensors 16 further include sensors to determine performance data about the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 further include at least one of an engine speed sensor, an engine torque sensor, a voltage and / or current sensor for the electric drive motor, an accelerator position sensor, a brake position sensor, a coolant temperature sensor, a cooling fan speed sensor, and a transmission oil temperature sensor.In another exemplary embodiment, the plurality of vehicle sensors 16 further include sensors to determine information about an environment within the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 further include at least one of the following components: a seat occupancy sensor, a cabin air temperature sensor, a cabin motion detection sensor, a cabin camera, a cabin microphone, and / or the like.In another exemplary embodiment, the plurality of vehicle sensors 16 further include sensors to determine information about the environment around the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 further include at least one of the following components: an ambient air temperature sensor, an air pressure sensor, and / or a photo and / or video camera positioned to view the environment in front of the vehicle 12.In another exemplary embodiment, at least one of the plurality of vehicle sensors 16 is a perception sensor capable of sensing objects and / or measuring distances in the environment around the vehicle 12. In a non-limiting example, the plurality of vehicle sensors 16 includes a stereoscopic camera with distance measurement capabilities. In one example, at least one of the plurality of vehicle sensors 16 is mounted within the interior of the vehicle 12, for example, on a headliner of the vehicle 12 as viewed through a windshield of the vehicle 12. In another example, at least one of the plurality of vehicle sensors 16 is mounted outside the vehicle 12, for example, on a roof of the vehicle 12, viewed from the environment around the vehicle 12. It should be understood that various additional types of perception sensors, such as, for example, LiDAR sensors, ultrasonic distance sensors, radar sensors, and / or time of flight sensors, are within the scope of the present disclosure. The plurality of vehicle sensors 16 are in electrical communication with the controller 14, as discussed above.Camera system 24 is a perception sensor that is used to capture images and / or videos of the environment around vehicle 12. In an exemplary embodiment, camera system 24 includes a photo and / or video camera positioned to view the environment around vehicle 12. In a non-limiting example, the camera system 24 includes a camera mounted within the vehicle 12, for example, on a headliner of the vehicle 12 as viewed through a windshield. In a further non-limiting example, the camera system 24 includes a camera mounted outside the vehicle 12, for example, on the roof of the vehicle 12, as viewed from the environment in front of the vehicle 12.In another exemplary embodiment, camera system 24 is an all-round view camera system that includes a plurality of cameras (also known as satellite cameras) arranged to provide a view of the adjacent environment to all sides of vehicle 12. In a non-limiting example, the camera system 24 includes a front facing camera (e.g., mounted in a grille of the vehicle 12), a rear facing camera (e.g., mounted on a rear tailgate of the vehicle 12), and two side facing cameras (e.g., mounted under each of the two side mirrors of the vehicle 12). In a further non-limiting example, the camera system 24 further includes an additional rear camera mounted proximate a high-mounted center stop lamp of the vehicle 12.It should be understood that camera systems with additional cameras and / or additional mounting locations are within the scope of the present disclosure. It should be further understood that cameras with various types of sensors, such as charge-coupled device (CCD) sensors, complementary metal oxide semiconductor (CMOS) sensors, and / or high dynamic range (HDR) sensors, are within the scope of the present disclosure. Moreover, cameras with various types of lenses, such as wide angle lenses and / or narrow angle lenses, are also within the scope of the present disclosure.The vehicle communication system 26 is utilized by the controller 14 to communicate with other systems external to the vehicle 12. For example, the vehicle communication system 26 includes capabilities for communicating with vehicles ("V2V" communication), infrastructure ("V2I" communication), remote systems at a remote call center (e.g., GENERAL MOTORS ON-STAR), and / or personal devices. Generally, the term vehicle-to-everything ("V2X") communication refers to communication between the vehicle 12 and any remote system (e.g., vehicles, infrastructure, and / or remote systems). In certain embodiments, the vehicle communication system 26 is a wireless communication system configured to communicate over a wireless local area network (WLAN) using IEEE 802.11 standards or using cellular data communication (e.g., using GSMA standards such as, for example, SGP.02, SGP.22, SGP.32, and the like). Accordingly, the vehicle communication system 26 may further include an embedded universal integrated circuit card (eUlCC) configured to store at least one cellular connectivity configuration profile, for example an embedded subscriber identity module (eNB) profile. The vehicle communication system 26 is further configured to communicate over a personal area network (e.g., BLUETOOTH and / or near field communication (NFC) of radio frequency communication). However, additional or alternative communication methods, such as a dedicated short-range communications (DSRC) channel and / or mobile telecommunication protocols based on the 3 rd generation partnership project (3GPP) standards, are also contemplated within the scope of the present disclosure. DSRC channels refer to short to medium range wireless or two-way communication channels specifically designed for use in automobiles, as well as a set of protocols and standards. The 3GPP is a partnership between several standardization organizations developing protocols and standards for mobile telecommunication. The 3GPP standards are structured as "releases". Thus, communication methods based on the 3GPP versions 14, 15, 16 and / or future 3GPP versions are considered to be within the scope of the present disclosure. Accordingly, the vehicle communication system 26 may include one or more antennas and / or transceivers for communication for receiving and / or transmitting signals such as cooperative sensing message (CSM) messages. The vehicle communication system 26 is configured to wirelessly communicate information between the vehicle 12 and another vehicle. Further, the vehicle communication system 26 is configured to wirelessly communicate information between the vehicle 12 and the infrastructure or other vehicles. It should be understood that the vehicle communication system 26 may be integrated with the controller 14 (e.g., on the same circuit board with the controller 14 or otherwise as part of the controller 14) without departing from the scope of the present disclosure.The GNSS 28 is used to determine a geographic location of the vehicle 12. In an exemplary embodiment, the GNSS 28 is a global positioning system (GPS). In a non-limiting example, the GPS includes a GPS receive antenna (not shown) and a GPS controller (not shown) in electrical communication with the GPS receiver antenna. The GPS receiver antenna receives signals from a plurality of satellites, and the GPS controller calculates the geographic location of the vehicle 12 based on the signals received from the GPS receiver antenna. In an exemplary embodiment, the GNSS 28 additionally includes a card. The map contains information about the infrastructure such as community boundaries, roads, rails, sidewalks, buildings, and the like. Therefore, the geographic location of the vehicle 12 is contextualized using the map information. In a non-limiting example, the map is retrieved from a remote source using a wireless connection. In another non-limiting example, the map is stored in a database of the GNSS 28. It should be understood that various additional types of satellite-based radio navigation systems, such as, for example, the Global Positioning System (GPS), Galileo, GLONASS, and the BeiDou Navigation Satellite System (BDS), are within the scope of the present disclosure. It should be understood that the GNSS 28 may be integrated with the controller 14 (e.g., on the same circuit board as the controller 14 or otherwise as part of the controller 14) without departing from the scope of the present disclosure.The automated driving system 18 is used to assist the occupant to increase the occupant's attention and / or control the behavior of the vehicle 12. Within the scope of the present disclosure, the automated driving system 18 includes systems that provide the occupant with any level of assistance (e.g., blind spot warning, lane keeping assist, and / or the like) as well as systems that are capable of autonomously controlling the vehicle 12 under some or all conditions (e.g., automatic lane keeping, adaptive cruise control, fully autonomous driving, and / or the like). It should be understood that all levels of automation of driving defined by, for example, SAE J3016 (i.e., SAE LEVEL 0, SAE LEVEL 1, SAE LEVEL 2, SAE LEVEL 3, SAE LEVEL 4, and SAE LEVEL 5) are within the scope of the present disclosure.In an exemplary embodiment, the automated driving system 18 is configured to detect and / or receive information about the environment around the vehicle 12 and process the information to provide assistance to the occupant. In some embodiments, the automated driving system 18 is a software module executing on the controller 14. In other embodiments, the automated driving system 18 includes a separate automated driving system controller, similar to controller 14, that is capable of processing the information about the environment around the vehicle 12. In an exemplary embodiment, automated driving system 18 may operate in a manual mode of operation, a semi-automated mode of operation, and a fully automated mode of operation.Within the scope of the present disclosure, the manual mode of operation means that the automated driving system 18 alerts or provides notifications to the occupant, but does not directly intervene or control the vehicle 12. In a non-limiting example, the automated driving system 18 receives information from the plurality of vehicle sensors 16. Using techniques such as computer vision, for example, the automated driving system 18 understands the environment around the vehicle 12 and provides assistance to the occupant. For example, if the automated driving system 18 identifies that the vehicle 12 is likely to collide with a remote vehicle based on the data from the plurality of vehicle sensors 16, the automated driving system 18 may use a display to give an alert to the occupant.As used herein, the semi-automated mode of operation means that the automated driving system 18 alerts or provides notifications to the occupant and may intervene directly in certain situations or control the vehicle 12. In a non-limiting example, the automated driving system 18 is additionally in electrical communication with components of the vehicle 12 such as a brake system, a propulsion system, and / or a steering system of the vehicle 12 such that the automated driving system 18 may control the behavior of the vehicle 12. In a non-limiting example, the automated driving system 18 may control the behavior of the vehicle 12 by applying the brakes of the vehicle 12 to avoid an imminent collision. In another non-limiting example, the automated driving system 18 may control the steering system of the vehicle 12 to provide an automatic lane keeping function. In a further non-limiting example, the automated driving system 18 may control the braking system, the propulsion system, and the steering system of the vehicle 12 to temporarily drive the vehicle 12 to a predetermined destination. However, intervention by the occupant may be necessary at any time. In an exemplary embodiment, automated driving system 18 may include additional components such as an eye tracking device configured to monitor an occupant's attention level and ensure that the occupant is ready to take control of vehicle 12.As used herein, the fully automatic operating mode means that the automated driving system 18 uses data from the plurality of vehicle sensors 16 to understand the environment and control the vehicle 12 to drive the vehicle 12 to a predetermined destination without requiring occupant control or intervention.The automated driving system 18 operates with a path planning algorithm configured to generate a safe and efficient trajectory for the vehicle 12 to navigate around the vehicle 12. In an exemplary embodiment, the path planning algorithm is a machine learning algorithm trained to output control signals for the vehicle 12 based on input data from the plurality of vehicle sensors 16. In another exemplary embodiment, the path planning algorithm is a deterministic algorithm programmed to output control signals for the vehicle 12 based on data from the plurality of vehicle sensors 16.In a non-limiting example, the path planning algorithm generates a sequence of waypoints or a continuous path that the vehicle 12 should follow to reach a destination while observing rules, regulations, and safety constraints. The sequence of waypoints or the continuous path is generated based at least in part on a detailed map and a current state of the vehicle 12 (i.e., position, speed, and orientation of the vehicle 12). The detailed map includes, for example, information about lane boundaries, road geometry, speed boundaries, traffic signs, and / or other relevant features. In an exemplary embodiment, the detailed map is stored in the media 22 of the controller 14 and / or in a remote database or server. In another exemplary embodiment, the path planning algorithm performs perception and mapping tasks to interpret the data collected from the plurality of vehicle sensors 16 and create, update, and / or expand the detailed map. In some examples, the path planning algorithm may be configured to generate multiple possible trajectories. An optimal trajectory is then selected for use by the automated driving system 18, as will be explained in more detail below.It should be understood that the automated driving system 18 may be any software and / or hardware module configured to operate in the manual operating mode, the semi-automated operating mode, or the fully automated operating mode as described above.Referring to FIG. 2, a schematic illustration of a first example road scene 30 acan be seen. The first example road scene 30 aincludes the vehicle 12 and a remote vehicle 32. the first example road scene 30 aincludes a predicted trajectory 34 of the remote vehicle 32. The predicted trajectory 34 includes a plurality of nodes of the predicted trajectory 34. In an exemplary embodiment, the plurality of nodes 36 of the predicted trajectory describe points along the predicted trajectory 34 and are located based on a predicted speed of the remote vehicle 32. In a non-limiting example, the plurality of nodes 36 of the predicted trajectory are located such that, based on the predicted speed of the remote vehicle 32, the time required to travel between any two adjacent nodes 36 of the predicted trajectory is equal to a predetermined node distance time (e.g., one second). While the first example road scene 30 aincludes a single remote vehicle 32, it should be understood that the system 10 and method 100 of the present disclosure are also applicable to multi-remote vehicle situations.The first example road scene 30 afurther includes a plurality of possible trajectories 38 for the vehicle 12. The plurality of possible trajectories 38 includes a plurality of nodes 40 of possible trajectories. In an example embodiment, the plurality of nodes 40 of possible trajectories describe points along the plurality of possible trajectories 38 and is located based on a speed of the vehicle 12. In a non-limiting example, the plurality of possible trajectory nodes 40 are located such that, based on the speed of the vehicle 12, the time required to travel between any two adjacent possible trajectory nodes 40 is equal to a predetermined node-distance time (e.g., one second). A first subset 40 aof the plurality of nodes 40 of possible trajectories originates from a first possible trajectory 38 aof the plurality of possible trajectories 38. a second subset 40 bof the plurality of nodes 40 of possible trajectories originates from a second possible trajectory 38 bof the plurality of possible trajectories 38. in the first example road scene 30 a, the plurality of possible trajectories 38 comprises two possible trajectories 38 (i.e. the first possible trajectory 38 aand the second possible trajectory 38 b). However, it should be understood that the plurality of possible trajectories 38 may include more than two possible trajectories without departing from the scope of the present disclosure.Referring to FIG. 3, a flowchart of the method 100 for path planning for a vehicle is presented. Referring to FIG. 3, and with continued reference to FIG. 2, the method 100 begins at block 102 and proceeds to block 104. In block 104, the controller 14 determines the predicted trajectory 34 of the remote vehicle 32. In an example embodiment, to determine the predicted trajectory 34, the controller 14 uses the plurality of vehicle sensors 16 to receive information about the remote vehicle 32. In a non-limiting example, the information includes one or more measurements of the remote vehicle 32, for example, a position of the remote vehicle 32, a speed of the remote vehicle 32, a heading of the remote vehicle 32, and / or the like.In another exemplary embodiment, the controller 14 utilizes the vehicle communication system 26 to receive information about the remote vehicle 32. In a non-limiting example, the controller 14 utilizes the vehicle communication system 26 to receive V2V (vehicle-to-vehicle) communication from the remote vehicle 32. The V2V communication may include information such as, for example, a position of the remote vehicle 32, a speed of the remote vehicle 32, a heading of the remote vehicle 32, an intended path of the remote vehicle 32, and / or the like. In another non-limiting example, the controller 14 utilizes the vehicle communication system 26 to receive V2I (vehicle-to-infrastructure) communication from the infrastructure (e.g., a traffic control device, a traffic camera, and / or the like) that includes information about the remote vehicle 32.The controller 14 then uses a path prediction algorithm to determine the predicted trajectory 34 based at least in part on the information about the remote vehicle 32. In a non-limiting example, the path prediction algorithm is a deterministic algorithm that uses a series of rules to determine the predicted trajectory 34 based on the information about the remote vehicle 32. In another non-limiting example, the path prediction algorithm is a machine learning algorithm such as, for example, a convolutional neural network (CNN), a gain learning algorithm, and / or the like. After block 104, the method 100 proceeds to block 106.In block 106, the controller 14 determines the plurality of possible trajectories 38 for the vehicle 12. In an exemplary embodiment, to determine the plurality of possible trajectories 38, the controller 14 uses the plurality of vehicle sensors 16 to receive information about an environment around the vehicle 12. The controller 14 then uses the path planning algorithm of the automated driving system 18 to generate the plurality of possible trajectories 38 based on the information about the environment around the vehicle 12.In an exemplary embodiment, a check is performed for the vicinity of the plurality of possible trajectories 38. The proximity check includes removing trajectories from the plurality of possible trajectories 38 that are too close to the predicted trajectory 34. In a non-limiting example, if a given node (e.g., a first node) of the plurality of possible trajectory nodes 40 is within a predetermined distance to any node (e.g., a first node) of the plurality of predicted trajectory nodes 36, the one of the plurality of possible trajectories 38 including the given node is determined to be too close and removed from the plurality of possible trajectories 38. In a non-limiting example, the predetermined distance is a predetermined minimum stopping distance required for the vehicle 12 to come to a full stop based at least in part on weather and / or road surface conditions.In an exemplary embodiment, the predetermined minimum stopping distance required for the vehicle 12 to stop is calculated using a formula: where D p is the predetermined minimum stopping distance, d BR is a brake reaction distance (i.e., a distance travelled by the vehicle 12 before the brakes are applied), d B is a braking distance (i.e., a distance travelled by the vehicle 12 while the brakes are applied), V is a longitudinal speed of the vehicle 12, t is a brake reaction time, and μ is a friction coefficient of the road surface. The coefficient of friction of the road surface is based at least in part on the weather and / or road surface conditions.It is contemplated that in some examples, trajectories that include traffic violations, for example, trajectories that require the vehicle 12 to exit a lane boundary, are optimal when alternative trajectories are associated with a high risk of collision. Therefore, trajectories that include traffic violations but that are not too close to the predicted trajectory 34 are not removed from the plurality of possible trajectories 38 during the close-by check.After block 106, the method 100 proceeds to blocks 108, 110, and 112 to determine one or more evaluation metrics of each of the plurality of nodes 40 of possible trajectories. In an example embodiment, the one or more evaluation metrics include a collision potential score, a maneuverability score, and a traffic violation score. Within the scope of the present disclosure, the one or more evaluation metrics are metrics used to quantify an optimality of each of the plurality of possible trajectories 38.In block 108, the controller 14 determines the collision potential score of each of the plurality of possible trajectory nodes 40, as discussed in more detail below. After block 108, the method 100 proceeds to block 114, which is discussed in more detail below.In block 110, the controller 14 determines the maneuverability score of each of the plurality of nodes 40 of possible trajectories, as discussed in more detail below. After block 110, the method 100 proceeds to block 114, which is discussed in more detail below.In block 112, the controller 14 determines the traffic violation score of each of the plurality of nodes 40 of possible trajectories. Within the scope of the present disclosure, the traffic violation score quantitates the legitimacy of maneuvering the vehicle 12 to each of the plurality of nodes 40 of possible trajectories. In a non-limiting example, a higher traffic violation score indicates that maneuvering the vehicle 12 to a given one of the plurality of nodes of possible trajectories is more illegal. In an exemplary embodiment, to determine the traffic violation score, the controller 14 evaluates the location of each of the plurality of nodes 40 of possible trajectories from the known road geometry (e.g., from the detailed map used by the path planning algorithm of the automated driving system 18 and / or the map included in the GNSS 28).In a non-limiting example, if a first given node of the plurality of possible trajectory nodes 40 is in a traffic lane of oncoming traffic (i.e., requires the vehicle 12 to travel in the wrong direction on a one-way roadway), the traffic violation score of the first given node is determined to be one (1). If a second given node of the plurality of possible trajectory nodes 40 is in a non-road region (i.e., requires the vehicle 12 to exit the roadway and travel on a side lane, median lane, sidewalk, and / or the like), the traffic violation score of the second given node is determined to be half (0.5). If a third given node of the plurality of possible trajectory nodes 40 is not in a non-road area and not in a traffic lane of the oncoming traffic, the traffic violation score of the third given node is determined to be zero (0).It should be understood that the above scenarios and traffic violation scores are only exemplary in nature. The traffic violation score may be a continuous value without departing from the scope of the present disclosure. The traffic violation score may be determined using additional methods, including, for example, a machine learning algorithm trained to determine traffic violation scores, a rule-based algorithm configured to determine traffic violation scores based on a known set of traffic rules, and / or the like, without departing from the scope of the present disclosure. Moreover, it is contemplated that in some examples, trajectories that include traffic violations, e.g., trajectories that require the vehicle 12 to exit a lane boundary, may be optimal when alternative trajectories are associated with a high risk of collision. After block 112, the method 100 proceeds to block 114.At block 114, the controller 14 selects an optimal trajectory based at least in part on the one or more evaluation metrics (i.e., the collision potential score, the maneuverability score, and the traffic violation score). In an exemplary embodiment, the optimal trajectory is a subset of the plurality of possible trajectory nodes 40 that minimizes a sum of the collision potential score, maneuverability score, and traffic violation score of each of the subset of the plurality of possible trajectory nodes 40. In a non-limiting example, the subset of the plurality of possible trajectory nodes 40 is selected to minimize a target function: where c is the target function, N is a set of nodes in the subset of the plurality of possible trajectory nodes 40, p is i the collision potential score of an ithnode of the subset of the plurality of possible trajectory nodes 40, v is i the traffic violation score of the ithnode of the subset of the plurality of possible trajectory nodes 40, and m is i the maneuverability score of the ithnode of the subset of the plurality of possible trajectory nodes 40.The objective function includes two constraints. The first constraint is that each of the subset of the plurality of possible trajectory nodes 40 originate from a same one of the plurality of possible trajectories 38. This means that the optimal trajectory may contain only nodes of one of the plurality of possible trajectories 38. The second constraint is that a first node of the subset of the plurality of possible trajectory nodes 40 and a second node of the subset of the plurality of possible trajectory nodes 40 must have a collision potential score of less than one. This means that the optimal trajectory may be only one of the plurality of possible trajectories 38 that does not result in a collision with the remote vehicle 32 with the first two nodes of the subset of the plurality of nodes 40 of possible trajectories.The factor devaluates the evaluation metrics of nodes of the subset of the plurality of nodes 40 of possible trajectories that are further from the vehicle 12. Therefore, the objective function is particularly directed to the metrics of scores of nodes proximate to the vehicle 12 because the metrics of scores of nodes farther from the vehicle 12 may change as the vehicle 12 approaches and more and / or new information is received.It should be understood that any mathematical optimization technique used to find one of the plurality of possible trajectories 38 that minimizes the objective function (Equation 1), including, for example, iterative methods (e.g., Newton's method, gradient descent, and / or the like), heuristic methods (e.g., differential evolution, genetic algorithms, hill-slicing algorithms, and / or the like), and / or the like, are within the scope of the present disclosure. After block 114, the method 100 proceeds to block 116.In block 116, the controller 14 performs a first action. In an exemplary embodiment, the first action includes adjusting operation of the automated driving system 18. In a non-limiting example, the path planning algorithm is adjusted such that the optimal trajectory selected at block 114 is used for further control and navigation of the vehicle 12. It is contemplated that in some examples, trajectories that include traffic violations, for example, trajectories that require the vehicle 12 to exit a lane boundary, may be optimal when alternative trajectories are associated with a high risk of collision. Thus, in some examples, the vehicle 12 performs an action including a traffic violation corresponding to the optimal trajectory selected in block 114 in block 116.In another exemplary embodiment, the first action includes the controller 14 transmitting the optimal trajectory to external systems, such as remote vehicles (e.g., remote vehicle 32), traffic control infrastructure, remote server systems, and / or the like, using the vehicle communication system 26. Remote systems that receive the optimal trajectory from the vehicle 12 may be configured to adjust their operation based on the optimal trajectory of the vehicle 12. For example, remote systems receiving the optimal trajectory from the vehicle 12 may be configured to perform actions to reduce the risk of collision, optimize traffic flow, provide information to road users, and / or the like. After block 116, the method 100 continues to enter a standby state in block 118.In an exemplary embodiment, the controller 14 repeatedly exits the standby state 118 and restarts the method 100 at block 102. In a non-limiting example, the controller 14 exits the ready state 118 and restarts the method 100 after a timer, for example, every three hundred milliseconds.Referring to FIG. 4, a schematic illustration of a second example road scene 30 bincluding uncertainties is seen. The second example road scene 30 bincludes all of the elements of the first example road scene 30 a(i.e., the vehicle 12, the remote vehicle 32, the predicted trajectory 34, and the plurality of possible trajectories 38). However, the second example road scene 30 bfurther includes a plurality of uncertainties 50. Within the scope of the present disclosure, each of the plurality of uncertainties 50 is a two-dimensional region around each of the plurality of nodes 36 of the predicted trajectory and each of the plurality of nodes 40 of possible trajectories that defines an uncertainty of the position of each of the plurality of nodes 36 of the predicted trajectory and each of the plurality of nodes 40 of possible trajectories. The plurality of uncertainties 50 comprises a plurality of uncertainties 50 aof the predicted trajectory and a plurality of uncertainties 50 bof possible trajectories.Referring to FIG. 5, a flowchart of an example embodiment 108 aof block 108 is illustrated. The example embodiment 108 abegins at block 502. Referring to FIGS. 4 and 5, in block 502, the controller 14 determines the plurality of uncertainties 50. In an exemplary embodiment, each of the plurality of uncertainties 50 is a two-dimensional Gaussian probability distribution. In a non-limiting example, one or more parameters of the two-dimensional Gaussian probability distribution (e.g., a covariance matrix) for each of the plurality of uncertainties 50 are determined based at least in part on weather conditions and / or road surface conditions in the environment around the vehicle 12. In another non-limiting example, the one or more two-dimensional Gaussian probability distribution parameters for each of the plurality of uncertainties 50 are determined based on a known error of one or more of the plurality of vehicle sensors 16 (e.g., a known localization error of the GNSS 28).In an exemplary embodiment, the weather conditions are determined using the plurality of vehicle sensors 16. In a non-limiting example, the camera system 24 is used to capture one or more images of the environment around the vehicle 12 and the weather conditions are inferred from the one or more images. In another non-limiting example, the vehicle communication system 26 is utilized to receive weather information from a remote vehicle (e.g., the remote vehicle 32) and / or a remote server system. Inclement weather and / or poor road surface conditions, the position uncertainty of the vehicle 12 and / or the remote vehicle 32 may be higher due to the extended stopping distance, greater turning radii, loss of traction, and / or the like.It should be understood that additional methods for determining parameters of the plurality of uncertainties 50 may be used without departing from the scope of the present disclosure, including, for example, systems and methods described in U.S. Application No. filed on Jun. 15, 2023. US 2024 / 0 416 929 A1, entitled "END-TO-END PERCEPTION PERTURBATION MODELING SYSTEM FOR A VEHICLE", the entire contents of which are hereby incorporated by reference. After block 502, the example embodiment 108 aproceeds to blocks 504 and 506.In block 504, the controller 14 determines a longitudinal distortion for each of the plurality of nodes 40 of possible trajectories and each of the plurality of nodes 36 of the predicted trajectory. In an example embodiment, the longitudinal distortion is determined based at least in part on modeling of the driving behavior when following a vehicle in the longitudinal direction. In one non-limiting example, the Gips model is used to determine longitudinal distortion. In another non-limiting example, the longitudinal distortion is determined based at least in part on the weather conditions in the environment around the vehicle 12, such as in U.S. Application No. filed on Jun. 23, 2023. US 2024 / 0 425 050 A1, entitled "PROBABILISTIC DRIVING BEHAVIOR MODELING SYSTEM FOR A VEHICLE", the entire contents of which are hereby incorporated by reference. After block 504, the example embodiment 108 aproceeds to block 508, which is discussed in more detail below.In block 506, the controller 14 determines a lateral distortion for each of the plurality of nodes 40 of possible trajectories and each of the plurality of nodes 36 of the predicted trajectory. In an exemplary embodiment, the lateral distortion is determined based at least in part on a dynamic-based model that takes into account weather conditions in the environment around the vehicle 12. In a non-limiting example, the dynamic-based model is a mathematical model of a vehicle that can determine lateral motion of the vehicle 12 based on a plurality of parameters. Therefore, the dynamic-based model outputs lateral distortion for each of the plurality of nodes 40 of possible trajectories and each of the plurality of nodes 36 of the predicted trajectory based on the plurality of parameters.The plurality of parameters include vehicle parameters such as, for example, vehicle speed, vehicle acceleration, vehicle steering angle, vehicle braking force, tire tread wear, tire temperature, and / or the like. The plurality of parameters also include parameters of weather and / or road surface conditions such as, for example, road surface temperature, road surface humidity, amount of standing water, snow, gray scale or hagle on the road surface, rate of precipitation on the road surface, wind speed, and / or the like. In an example embodiment, the plurality of parameters are received using the plurality of vehicle sensors 16.In an exemplary embodiment, the dynamic-based model uses the Burckhardt method to determine lateral distortion of the vehicle based on the weather and / or road surface conditions, as described in "An Observer of Tire-Road Forces and Friction for Active Security Vehicle Systems" by Baffet et al. (IEEE / ASME TRANSACTIONS ON MECHATRONICS, BD. 12, No. 6, pp. 651-661, Dec. 2007), the entire contents of which are hereby incorporated by reference. After block 506, the example embodiment 108 aproceeds to block 508.Referring to FIG. 6, a schematic illustration of a third example road scene 30c of inclusively distorted uncertainties is seen. The third example road scene 30 cincludes all of the elements of the first example road scene 30 a(i.e., the vehicle 12, the remote vehicle 32, the predicted trajectory 34, and the plurality of possible trajectories 38). However, the third example road scene 30 cfurther includes a plurality of distorted uncertainties 60. Within the scope of the present disclosure, each of the plurality of distorted uncertainties 60 is a two-dimensional region around each of the plurality of nodes 36 of the predicted trajectory and each of the plurality of nodes 40 of possible trajectories that defines a distorted uncertainty of the position of each of the plurality of nodes 36 of the predicted trajectory and each of the plurality of nodes 40 of possible trajectories based on the longitudinal distortion determined in block 504 and the lateral distortion determined in block 506. The plurality of distorted uncertainties 60 comprises a plurality of distorted uncertainties 60 aof the predicted trajectory and a plurality of distorted uncertainties 60 bof possible trajectories.Referring to FIGS. 5 and 6, in block 508, the controller 14 generates the plurality of distorted uncertainties 60 aof the predicted trajectory by applying the longitudinal distortion determined in block 504 and the lateral distortion determined in block 506 to each of the plurality of uncertainties 50 aof the predicted trajectory. The controller 14 further generates the plurality of distorted uncertainties 60 bof possible trajectories by applying the longitudinal distortion determined in block 504 and the lateral distortion determined in block 506 to each of the plurality of uncertainties 50 bof possible trajectories.In an exemplary embodiment, the longitudinal distortion and the lateral distortion are applied to each of the plurality of uncertainties 50 using an equation: wherein one of the plurality of distorted uncertainties is 60, I is an identity matrix, one of the plurality of uncertainties 50, H k is a measurement model, and R k is one of the longitudinal distortions determined in block 504, or the lateral distortion determined in block 506. Equation 3 is used for each of the plurality of uncertainties 50 twice, once to apply the longitudinal distortion and once to apply the lateral distortion. After block 508, the example embodiment 108 aproceeds to block 510.In block 510, the controller 14 determines the collision potential score for each of the plurality of nodes 40 of possible trajectories based at least in part on the plurality of distorted uncertainties 60 aof the predicted trajectory and the plurality of uncertainties 50 aof the predicted trajectory. In an exemplary embodiment, the collision potential score for each of the plurality of possible trajectory nodes 40 is defined by: where p i is the collision potential score for an i-th node of the plurality of possible trajectory nodes 40, r i is one of the plurality of distorted predicted trajectory uncertainties 60 acorresponding to an i-th node of the plurality of nodes 36 of the predicted trajectory, e i is one of the plurality of distorted possible trajectory uncertainties 60 bcorresponding to the i-th node of the plurality of possible trajectory nodes 40, n is an intersection operator, and U is an merging operator. In other words, the collision potential score refers to an overlap of the plurality of distorted uncertainties 60 aof the predicted trajectory with the plurality of distorted uncertainties 60 bof possible trajectories. After block 510, the example embodiment 108 ais complete and the method 100 continues as described above.Referring to FIG. 7, a flowchart of an example embodiment 110 aof block 110 is illustrated. The example embodiment 110 abegins with blocks 702 and 704. At block 702, the controller 14 determines a maximum torque available from a propulsion system (e.g., an engine, a hybrid electric drive, a full electric drive, and / or the like) of the vehicle 12. In an exemplary embodiment, the maximum available torque is predetermined and stored in the media 22 of the controller 14. After block 702, the example embodiment 110 aproceeds to block 706, as discussed in more detail below.In block 704, the controller 14 determines an estimated propulsion system torque required to reach each of the plurality of nodes 40 of possible trajectories. In an exemplary embodiment, the estimated propulsion system torque required to reach a given node is determined based at least in part on a dynamic-based model that takes into account weather conditions in the environment around the vehicle 12. In a non-limiting example, the dynamic-based model is a mathematical model of a vehicle that can determine the estimated propulsion system torque required to reach a given node based on a plurality of parameters. Therefore, the dynamic-based model outputs the estimated propulsion system torque for each of the plurality of nodes 40 of possible trajectories. It should be appreciated that in some examples, one or more of the plurality of nodes 40 of possible trajectories may be in ranges that are impossible to achieve regardless of the available torque. Therefore, in cases where one or more of the plurality of nodes 40 of possible trajectories are located in, for example, a body of water, are obstructed by a wall or other obstacle, and / or the like, the maneuverability score is determined to be a very large value (e.g., infinity).The plurality of parameters include terrain parameters such as, for example, the type of road surface (e.g., paved, dirt, sand, and / or the like), the condition of a road surface (e.g., broken road surface, newly renewed road surface, and / or the like), lane height differences, and / or the like. The plurality of parameters also include weather parameters such as, for example, the temperature of the road surface, the moisture content of the road surface, the amount of standing water, snow, gray scale, or hagle on the road surface, the rate of precipitation on the road surface, the wind speed, and / or the like. In an example embodiment, the plurality of parameters are received using the plurality of vehicle sensors 16. After block 704, the example embodiment 110 aproceeds to block 706.At block 706, the controller 14 determines the maneuverability score for each of the plurality of possible trajectory nodes 40 using a formula: where m is i the maneuverability score for an i-th node of the plurality of possible trajectory nodes 40, T is i the estimated propulsion system torque determined at block 704 required to reach the i-th node of the plurality of possible trajectory nodes 40, and T is MAX the maximum available propulsion system torque of the vehicle as determined at block 702. After block 706, the example embodiment 110 ais complete and the method 100 continues as described above.The system 10 and method 100 of the present disclosure provide several advantages. By considering the weather and / or road surface conditions to determine the optimal trajectory, the system 10 and method 100 of the present disclosure provide increased performance of the automated driving system 18, thus increasing perception and comfort of occupants of the vehicle 12. Moreover, it is contemplated that in some examples, trajectories including non-conventional maneuvers, for example, traffic violations, may be optimal when alternative trajectories are associated with a high risk of collision. Therefore, the system 10 and method 100 of the present disclosure enable consideration of avoidance maneuvers or other non-conventional maneuvers that may be necessary to avoid collision in bad weather situations.

Claims

A method (100) of path planning for a vehicle (12), the method (100) comprising: determining (104) a predicted trajectory (34) of a remote vehicle (32), the predicted trajectory (34) of the remote vehicle (32) comprising a plurality of nodes (36) of the predicted trajectory; determining (106) a plurality of possible trajectories (38) for the vehicle (12), the plurality of possible trajectories (38) comprising a plurality of nodes (40) of possible trajectories; determining (108, 110, 112) one or more evaluation metrics of each of the plurality of nodes (40) of possible trajectories based at least in part on a weather condition in an environment around the vehicle (12); selecting (114) an optimal trajectory for the vehicle (12) from the plurality of possible trajectories (38) based at least in part on the one or more evaluation metrics of each of the plurality of nodes (40) of possible trajectories; Performing (116) a first action based at least in part on the optimal trajectory, wherein the one or more evaluation metrics further comprise a collision potential score, and wherein determining (108) the collision potential score for each of the plurality of nodes (40) of possible trajectories further comprises: determining (502) a plurality of uncertainties (50), wherein the plurality of uncertainties (50) comprises a plurality of uncertainties (50a) of the predicted trajectory and a plurality of uncertainties (50b) of possible trajectories, wherein each of the plurality of uncertainties (50a) of the predicted trajectory corresponds to one of the plurality of nodes (36) of the predicted trajectory, and wherein each of the plurality of uncertainties (50b) of possible trajectories corresponds to one of the plurality of nodes (40) of possible trajectories; determining (508) a plurality of distorted uncertainties (60a) of the predicted trajectory and a plurality of distorted uncertainties (60b) of possible trajectories by applying a distortion to each of the plurality of uncertainties (50a) of the predicted trajectory and each of the plurality of uncertainties (50b) of possible trajectories, wherein the distortion is based at least in part on the weather condition in the environment around the vehicle (12); determining (510) the collision potential score for each of the plurality of nodes (40) of possible trajectories based at least in part on the plurality of distorted uncertainties (60a) of the predicted trajectory and the plurality of distorted uncertainties (60b) of possible trajectories, wherein the one or more evaluation metrics further comprise a maneuverability score, and wherein determining (110) the maneuverability score for each of the plurality of nodes (40) of possible trajectories further comprises: determining (704) an estimated propulsion system torque required to reach each of the plurality of nodes (40) of possible trajectories; determining (706) the maneuverability score for each of the plurality of possible trajectory nodes (40) based at least in part on the estimated propulsion system torque using a formula: m i = T i T M A X wherein m i is the maneuverability score for an i-th node of the plurality of possible trajectory nodes (40), T i is the estimated propulsion system torque required to reach the i-th node of the plurality of possible trajectory nodes (40), and T MAX is a maximum torque available from a propulsion system of the vehicle (12), wherein the one or more evaluation metrics further comprise a traffic violation score, and wherein the traffic violation score quantitates a legibility of maneuvering the vehicle (12) to each of the plurality of nodes (40) of possible trajectories, wherein selecting (114) the optimal trajectory further comprises: selecting a subset of the plurality of nodes (40) of possible trajectories as the optimal trajectory, wherein the subset of the plurality of nodes (40) of possible trajectories is selected to minimize a target function: c = ∑ i = 0 N N N - i N molar ( p i + v i + m i ) wherein c is the target function, N is a set of nodes in the subset of the plurality of nodes (40) of possible trajectories, p i is the collision potential score of an i-th node of the subset of the plurality of possible trajectory nodes (40), υ i is the traffic violation score of the i-th node of the subset of the plurality of possible trajectory nodes (40), and mis i is the maneuverability score of the i-th node of the subset of the plurality of possible trajectory nodes (40), wherein each of the subset of the plurality of possible trajectory nodes (40) originates from a same one of the plurality of possible trajectories (38), and wherein a first node of the subset of the plurality of possible trajectory nodes (40) and a second node of the subset of the plurality of possible trajectory nodes (40) have a collision potential score of less than one.The method (100) of claim 1, wherein determining (502) the plurality of uncertainties (50) further comprises: determining the plurality of uncertainties (50) based on at least one of the following factors: the weather condition in the environment around the vehicle (12) and a location error of one or more of a plurality of vehicle sensors (16).The method (100) of claim 1, wherein determining (508) the plurality of distorted uncertainties (60a) of the predicted trajectory and the plurality of distorted uncertainties (60b) of possible trajectories further comprises: determining (504) a longitudinal distortion for each of the plurality of nodes (40) of possible trajectories and each of the plurality of nodes (36) of the predicted trajectory; determining (506) a lateral distortion for each of the plurality of nodes (40) of possible trajectories and each of the plurality of nodes (36) of the predicted trajectory, wherein the lateral distortion is determined based at least in part on a dynamic-based model and the weather condition in the environment around the vehicle (12); applying (508) the longitudinal distortion and the lateral distortion to each of the plurality of uncertainties (60a) of the predicted trajectory; and applying (508) the longitudinal distortion and the lateral distortion to each of the plurality of uncertainties (60b) of possible trajectories.The method (100) of claim 1, wherein determining (108) the collision potential score for each of the plurality of nodes (40) of possible trajectories further comprises: determining (510) the collision potential score using a formula: p i = r i ∩ e i r i i e i wherein p i is the collision potential score for an i-th node of the plurality of nodes (40) of possible trajectories, r is one of the plurality of distorted uncertainties (60a) of the predicted trajectory corresponding to an i-th node of the plurality of nodes (36) of the predicted trajectory, e i is one of the plurality of distorted uncertainties (60b) of possible trajectories, which corresponds to the ith node of the plurality of nodes (40) of possible trajectories, n is an intersection operator and U is a merging operator.The method (100) of claim 1, wherein selecting (114) the optimal trajectory further comprises: selecting a subset of the plurality of possible trajectory nodes (40) as the optimal trajectory, wherein the subset of the plurality of possible trajectory nodes (40) is selected to minimize a sum of each of the one or more evaluation metrics of each of the subset of the plurality of possible trajectory nodes (40).The method (100) of claim 1, wherein performing (116) the first action further comprises: adjusting operation of an automated driving system (18) of the vehicle (12) based at least in part on the optimal trajectory.

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