Patent ID: 12240491

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses.

To choose a path for an autonomous or semi-autonomous vehicle and/or to supplement advanced driver-assistance systems (ADAS) of a vehicle, the present disclosure provides a vehicle equipped with a motion planning system. The motion planning system accounts for a variety of environmental factors to choose an optimal path, for example, road geometry (e.g., lane width, intersection configuration, and the like), traffic laws, and position of other vehicles on the roadway. Advantageously, the motion planning system of the present disclosure further accounts for other vehicles on the roadway that exhibit hazardous and/or unpredictable behavior due to, for example, negligence, distraction, or emotional reactions (e.g., “road rage”).

Referring toFIG.1, a system for motion planning for a vehicle is illustrated and generally indicated by reference number10. The system10is shown with an exemplary vehicle12. While a passenger vehicle is illustrated, it should be appreciated that the vehicle12may be any type of vehicle without departing from the scope of the present disclosure. The system10generally includes a controller14, at least one vehicle sensor16, a global navigation satellite system (GNSS)18, a vehicle communication system20, and a display22.

The controller14is used to implement a method100for motion planning for a vehicle, as will be described below. The controller14includes at least one processor24and a non-transitory computer readable storage device or media26. The processor24may be a custom made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the controller14, a semiconductor-based microprocessor (in the form of a microchip or chip set), a macroprocessor, a combination thereof, or generally a device for executing instructions. The computer readable storage device or media26may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor24is powered down. The computer-readable storage device or media26may be implemented using a number of memory devices such as PROMs (programmable read-only memory), EPROMS (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or another electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller14to control various systems of the vehicle12. The controller14may also consist of multiple controllers which are in electrical communication with each other. The controller14may be inter-connected with additional systems and/or controllers of the vehicle12, allowing the controller14to access data such as, for example, speed, acceleration, braking, and steering angle of the vehicle12.

The controller14is in electrical communication with the at least one vehicle sensor16, the global navigation satellite system (GNSS)18, the vehicle communication system20, and the display22. In an exemplary embodiment, the electrical communication is established using, for example, a CAN bus, a Wi-Fi network, a cellular data network, or the like. It should be understood that various additional wired and wireless techniques and communication protocols for communicating with the controller14are within the scope of the present disclosure.

The at least one vehicle sensor16is used to gather information about an environment surrounding the vehicle12, including, for example, distance measurements between the vehicle12and a remote vehicle and/or capture images and/or videos of the environment surrounding the vehicle12. In an exemplary embodiment, the at least one vehicle sensor16is a photo and/or video camera which is positioned to view the environment in front of the vehicle12. In one example, the at least one vehicle sensor16is affixed inside of the vehicle12, for example, in a headliner of the vehicle12, having a view through a windscreen28of the vehicle12. In another example, the at least one vehicle sensor16is affixed outside of the vehicle12, for example, on a roof of the vehicle12, having a view of the environment in front of the vehicle12. It should be understood that cameras having various sensor types including, for example, 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. Furthermore, cameras having various lens types including, for example, wide-angle lenses and/or narrow-angle lenses are also within the scope of the present disclosure. Additionally, it should be understood that use of additional types of sensors, such as, for example, LIDAR, radar, ultrasonic distance measuring, an inertial measurement unit, and the like are within the scope of the present disclosure.

The GNSS18is used to determine a geographical location of the vehicle12on a map. In an exemplary embodiment, the GNSS18includes a GNSS receiver antenna (not shown) and a GNSS controller (not shown) in electrical communication with the GNSS receiver antenna. The GNSS receiver antenna receives signals from a plurality of satellites, and the GNSS controller calculates the geographical location of the vehicle12based on the signals received by the GNSS receiver antenna. In an exemplary embodiment, the GNSS18additionally includes a map. The map includes information about infrastructure such as municipality borders, roadways, railways, sidewalks, buildings, and the like. Therefore, the geographical location of the vehicle12is contextualized using the map information. The map further includes information, such as, for example, road type, road width, road markings (e.g., lane edges), road signage (e.g., road signs and traffic signals), road speed limit, road weather condition, and road lighting condition. 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 GNSS18.

The vehicle communication system20is used by the controller14to communicate with other systems external to the vehicle12. For example, the vehicle communication system20includes capabilities for communication with vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems at a remote call center (e.g., ON-STAR by GENERAL MOTORS) and/or personal devices. In the scope of the present disclosure, the term “V2X” refers to communication between the vehicle12and any remote system (e.g., vehicles, infrastructure, and/or remote systems). In certain embodiments, the vehicle communication system20is a wireless communication system configured to communicate via a wireless local area network (WLAN) using IEEE 802.11 standards or by using cellular data communication. However, additional or alternate communication methods, such as a dedicated short-range communications (DSRC) channel and/or mobile telecommunications protocols based on the 3rd Generation Partnership Project (3GPP) standards, are also considered within the scope of the present disclosure. DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards. The 3GPP refers to a partnership between several standards organizations which develop protocols and standards for mobile telecommunications. 3GPP standards are structured as “releases”. Thus, communication methods based on 3GPP release 14, 15, 16 and/or future 3GPP releases are considered within the scope of the present disclosure. Accordingly, the vehicle communication system20may include one or more antennas and/or communication transceivers for receiving and/or transmitting signals, such as cooperative sensing messages (CSMs). The vehicle communication system20is configured to wirelessly communicate information between the vehicle12and another vehicle. Further, the vehicle communication system20is configured to wirelessly communicate information between the vehicle12and infrastructure or other vehicles.

The display22is used to provide information to an occupant of the vehicle12. In the scope of the present disclosure, the occupant includes a driver and/or a passenger of the vehicle12. In the exemplary embodiment depicted inFIG.1, the display22is a human-machine interface (HMI) located in view of the occupant and capable of displaying text, graphics and/or images. It is to be understood that HMI display systems including LCD displays, LED displays, and the like are within the scope of the present disclosure. Further exemplary embodiments where the display22is disposed in a rearview mirror are also within the scope of the present disclosure. In another exemplary embodiment, the display22includes a head-up display (HUD) configured to provide information to the occupant by projecting text, graphics, and/or images upon the windscreen28. The text, graphics, and/or images are reflected by the windscreen28and are visible to the occupant without looking away from a roadway ahead of the vehicle12. In another exemplary embodiment, the display22includes an augmented reality head-up display (AR-HUD). The AR-HUD is a type of HUD configured to augment the occupant's vision of the roadway ahead of the vehicle12by overlaying text, graphics, and/or images on physical objects in the environment surrounding the vehicle12within a field-of-view of the occupant. It should be understood that additional systems for displaying information to the occupant of the vehicle12are also within the scope of the present disclosure.

Referring toFIG.2, a first exemplary driving environment30ais represented as a plurality of location cells32. In a non-limiting example, the first exemplary driving environment30aincludes a roadway having a plurality of lanes of travel. The first exemplary driving environment30aincludes the vehicle12and a remote vehicle34. Each of the plurality of location cells32is assigned a risk score. In the scope of the present disclosure, the risk score of one of the plurality of location cells32quantifies a level of risk for the vehicle12to enter the one of the plurality of location cells32. The risk score will be discussed in further detail below. InFIG.2and throughout the present disclosure, the risk score of each of the plurality of location cells32is represented by varying density of hatch fill. More specifically, location cells having a low risk score (e.g., between zero and twenty) are depicted with no hatch fill (e.g., location cell36a). Location cells having a moderate risk score (e.g., between twenty and forty) are depicted with a moderate-density hatch fill (e.g., location cell36b). Location cells having a high risk score (e.g., between forty and sixty) are depicted with a high-density hatch fill (e.g., location cell36c). Location cells having a very high risk score (e.g., between sixty and one hundred) are depicted with a very-high-density hatch fill (e.g., location cell36d). It should be understood that each of the plurality of location cells32are assigned risk scores continuously within a predetermined range (e.g., between zero and one hundred). The depiction of location cells having a low, moderate, high, and very high risk score is for illustrative purposes only and is not intended to limit the present disclosure. The first exemplary driving environment30adepicts an exemplary situation where the remote vehicle34is increasing speed and changing lanes erratically. Further exemplary driving environments will be discussed below in reference toFIGS.8aand8b.

Referring toFIG.3, the method100for motion planning for a vehicle begins at block102and proceeds to block104. At block104, a path determination algorithm is retrieved from the media26of the controller14. In an exemplary embodiment, the path determination algorithm stored in the media26of the controller14may be periodically updated by an over-the-air (OTA) update procedure, using, for example, the vehicle communication system20. A method400for training the path determination algorithm will be discussed in reference toFIG.4. After block104, the method100proceeds to block106.

At block106, the controller14performs a plurality of measurements of the remote vehicle34using the at least one vehicle sensor16. In an exemplary embodiment, the plurality of measurements includes at least a plurality of position measurements of the remote vehicle34relative to the vehicle12. Therefore, based on the plurality of measurements, the controller14may also compute a relative velocity, relative acceleration, heading, and location (using the GNSS18) of the remote vehicle34. After block106, the method100proceeds to block108.

At block108, the controller14determines the risk score for each of the plurality of location cells32in the first exemplary driving environment30abased at least in part on the plurality of measurements performed at block106. In the scope of the present disclosure, the risk score of one of the plurality of location cells32quantifies a level of risk for the vehicle12to enter the one of the plurality of location cells32. For example, referring again toFIG.2, the remote vehicle34is increasing speed and changing location rapidly within the first exemplary driving environment30a. Therefore, location cells in front of and adjacent to the remote vehicle34(e.g., the location cell36d) are assigned a high risk score (e.g., a risk score of eighty-five). In an exemplary embodiment, the risk score for each of the plurality of location cells32is determined using a risk score machine learning model. In a non-limiting example, an input to the risk score machine learning model includes the plurality of measurements performed at block106, and an output of the risk score machine learning model is a risk score for each of the plurality of location cells32. In a non-limiting example, the risk score machine learning algorithm is trained by providing the algorithm with a plurality of measurements which have been pre-classified to correspond with hazardous driving behaviors. After sufficient training of the risk score machine learning algorithm, the algorithm can determine risk scores for the plurality of location cells32. After block108, the method100proceeds to block110.

At block110, the controller14classifies a behavior of the remote vehicle34based at least in part on the plurality of measurements performed at block106. In an exemplary embodiment, a behavior class includes an intentional behavior class and an unintentional behavior class. In a non-limiting example, the intentional behavior class includes tailgating (i.e., following another vehicle at an unsafe distance), aggressive behavior (e.g., “road rage”), and wrong direction driving (i.e., driving in an incorrect direction on a one-way roadway or a controlled-access highway). The unintentional behavior class includes, for example, damage to the remote vehicle34(e.g., a non-functional brake indicator light) and distracted driving (e.g., using a cellphone while driving the remote vehicle34). In an exemplary embodiment, the behavior of the remote vehicle34is classified using a plurality of behavior classification machine learning models (as discussed in U.S. application Ser. No. 18/055,116 filed Nov. 14, 2022, which matured into U.S. Publication No. 2024/0157935 the entire disclosure of which is hereby incorporated by reference herein). After block110, if the behavior of the remote vehicle34is classified as intentional behavior, the method100proceeds to block112and a planned path of the vehicle12is adjusted using one of plurality of supervisory actions, as will be discussed below. If the behavior of the remote vehicle34is classified as unintentional behavior, the method100proceeds to block114.

At block112, the controller14determines a type of intentional behavior of the remote vehicle34. In an exemplary embodiment, a behavior type includes a tailgating behavior type, a road rage behavior type, and a wrong direction behavior type. The tailgating behavior type includes following another vehicle at an unsafe distance. The road rage behavior type includes aggressive driving behavior, such as, for example, sudden braking or acceleration near another remote vehicle, cutting off another remote vehicle, and/or causing another remote vehicle to take evasive action to avoid a collision. The wrong direction behavior type includes driving in an incorrect direction on a one-way roadway, a controlled-access highway, or the like. It should be understood that further intentional behavior types are also included in the scope of the present disclosure. In an exemplary embodiment, the behavior type of the remote vehicle34is determined using a plurality of behavior classification machine learning models (as discussed in U.S. application Ser. No. 18/055,116 filed Nov. 14, 2022). After block112, if the behavior type of the remote vehicle34is the tailgating behavior type, the method100proceeds to block116. If the behavior type of the remote vehicle34is the road rage behavior type, the method100proceeds to block118. If the behavior type of the remote vehicle34is the wrong direction behavior type, the method100proceeds to block120.

At block116, in response to determining that the behavior type of the remote vehicle34is the tailgating behavior type at block112, the controller14executes a tailgating supervisory action. The tailgating supervisory action will be discussed in further detail below in reference toFIG.5. After block116, the method100proceeds to block114.

At block118, in response to determining that the behavior type of the remote vehicle34is the road rage behavior type at block112, the controller14executes a road rage supervisory action. The road rage supervisory action will be discussed in further detail below in reference toFIG.6. After block118, the method100proceeds to block114.

At block120, in response to determining that the behavior type of the remote vehicle34is the wrong direction behavior type at block112, the controller14executes a wrong direction supervisory action. The wrong direction supervisory action will be discussed in further detail below in reference toFIG.7. the method100proceeds to block114.

At block114, the planned path of the vehicle12is adjusted using the path determination algorithm retrieved at block104. In an exemplary embodiment, the path determination algorithm receives as input a location of the vehicle12(determined, for example, using the GNSS18), a location of the remote vehicle34(determined, for example, using the plurality of measurements performed at block106), and the risk score for each of the plurality of location cells32determined at block108. In another exemplary embodiment, the path determination algorithm further receives as input a supervisory command provided by one of blocks116,118, and120, and adjusts the planned path based on the supervisory command. Training of the path determination algorithm will be discussed in further detail below in reference toFIG.4. After block114, the method100proceeds to enter the standby state at block122.

In an exemplary embodiment, the controller14repeatedly exits the standby state122and restarts the method100at block102. In a non-limiting example, the controller14exits the standby state122and restarts the method100on a timer, for example, every three hundred milliseconds.

Referring toFIG.4, a flowchart of the method400for training the path determination algorithm is provided. It should be understood that to train the path determination algorithm, a computer system other than the vehicle12and the controller14may be used, including, for example, a server computer, a cloud-based server farm, or the like. Once the path determination algorithm has been trained, the path determination algorithm is transferred to the controller14of the vehicle12such that the path determination algorithm may be retrieved by the controller14of the vehicle12at block104of the method100.

In the method400, the path determination algorithm is a reinforcement learning (RL) algorithm. The RL algorithm provides an RL agent which may take actions in the environment. The RL algorithm takes into account a variety of factors such as road geometry (e.g., lane width, intersection configuration, and the like), traffic laws, and position of the simulated remote vehicle (i.e., an observation space of the RL algorithm). In the method400, the RL algorithm is further trained to take the risk score for each of the plurality of location cells32into account. The set of possible actions which the vehicle12may take is known as the action space. The action space may include, for example, accelerating, decelerating, changing lanes, and the like. As will be discussed in further detail below, for each action taken, the RL agent receives an appropriate reward. Based on the observation space, the actions taken within the action space, and the computed rewards, the RL algorithm is trained to maximize the received reward. It should be understood that various additional training methods, such as, for example, supervised learning, semi-supervised learning, and unsupervised learning, are within the scope of the present disclosure. Furthermore, RL algorithms using a policy function instead of a machine learning model are also within the scope of the present disclosure.

The method400begins at block402and proceeds to blocks404and406. At block404, a simulated risk score (analogous to the risk score determined at block108) is determined for each of the plurality of simulated location cells in the simulated environment using the risk score machine learning algorithm discussed in reference to block108above. As indicated byFIG.4, block404is repeated constantly, such that the simulated risk score for each of the plurality of simulated location cells is constantly updated to account for motion of the simulated vehicle and/or the simulated remote vehicle.

At block406, a unique simulated environment is generated. The simulated environment includes the simulated host vehicle (analogous to the vehicle12), the simulated remote vehicle (analogous to the remote vehicle34), and a plurality of simulated location cells (analogous to the plurality of location cells32). In an exemplary embodiment, the simulated environment further includes a plurality of simulated remote vehicles, each of the plurality of simulated remote vehicles exhibiting differing driving behaviors. In an exemplary embodiment, a position of the simulated host vehicle relative to the simulated remote vehicle in the simulated environment is randomized, and a behavior of the simulated remote vehicle is also randomized. After block406, the method400proceeds to block408.

At block408, the simulated host vehicle takes an action within the action space. In a non-limiting example, the action space includes accelerating, decelerating, changing lanes, and the like. In an exemplary embodiment, the action is determined by the RL algorithm based on the simulated risk score for each of the plurality of simulated location cells and the variety of additional factors such as road geometry (e.g., lane width, intersection configuration, and the like), traffic laws, and position of the simulated remote vehicle. After block408, the method400proceeds to block410.

At block410, a total reward is calculated based on the action taken at block408. In an exemplary embodiment, the reward includes a sum of a first reward proportional to a distance between the simulated host vehicle and the simulated remote vehicle and a second reward proportional to the simulated risk score of one of the simulated location cells entered by the simulated host vehicle. It should be understood that the total reward is also calculated based on other factors (e.g., time to finish maneuvers, number of collisions and/or near-collisions, smoothness of driving maneuvers, and the like) such that the path determination algorithm is trained to effectively navigate a variety of environments. For example, a large negative reward is provided if the action taken at block408results in the simulated host vehicle leaving the roadway. It should be understood that the first and second rewards are computed for the purpose of training the RL algorithm to handle vehicles exhibiting non-standard driving behaviors. Additional rewards may be included in the total reward for the purpose of training the RL algorithm to effectively navigate a variety of environments. After block410, the method400proceeds to block412.

At block412, to train the RL algorithm, a decision-making function of the RL algorithm is updated based on the reward calculated at block410. In an exemplary embodiment, parameters of the path determination algorithm (e.g., weights, biases, and/or the like) are modified based on the reward calculated at block410such that the RL algorithm learns to maximize the total reward. In an exemplary embodiment where the RL algorithm uses a policy function, an RL policy function is updated at block412. After block412, the method400proceeds to block414.

At block414, a predetermined simulation stopping point is evaluated. In an exemplary embodiment, the predetermined simulation stopping point is evaluated based on at least one of a plurality of factors, including, for example, an average total reward collected over time and a convergence of the RL agent. If the predetermined simulation stopping point is reached, the method400proceeds to block416. If the predetermined simulation stopping point is not reached, the method400returns to block408, such that a further action is taken, and the decision-making function of the RL algorithm is further updated.

At block416, a predetermined training stopping point is evaluated. In an exemplary embodiment, the predetermined training stopping point is evaluated based on at least one of a plurality of factors, including, for example, a number of simulations performed, and a convergence of a total reward received during each simulation. If the predetermined training stopping point is reached, the method400proceeds to enter a standby state at block418. If the predetermined simulation stopping point is not reached, the method400returns to block404, such that a new, unique simulated environment is generated.

Referring toFIG.5, a flowchart of an exemplary embodiment of the block116discussed above is provided. The exemplary embodiment of block116is performed when a determination is made that the remote vehicle34is tailgating the vehicle12. The exemplary embodiment of block116begins at block116a. At block116a, a maximum allowed speed of the roadway is determined based at least in part on a speed limit of the roadway. In an exemplary embodiment, the maximum allowed speed is determined based on a speed limit of the roadway retrieved using the GNSS18and based on weather, visibility, and/or surface conditions determined by communication with an external system using the vehicle communication system20. After block116a, the exemplary embodiment of block116proceeds to block116b.

At block116b, a speed of the vehicle12is compared to the maximum allowed speed determined at block116a. If the speed of the vehicle is less than the maximum allowed speed, the exemplary embodiment of block116proceeds to block116c. If the speed of the vehicle is greater than or equal to the maximum allowed speed, the exemplary embodiment of block116proceeds to block116d.

At block116c, the speed of the vehicle is increased to the maximum allowed speed. After block116, the exemplary embodiment of block116proceeds to block116e.

At block116d, the speed of the vehicle is maintained at the maximum allowed speed. After block116, the exemplary embodiment of block116proceeds to block116e.

At block116e, the controller14identifies a state of a right adjacent lane of travel. In an exemplary embodiment, the right adjacent lane of travel is a lane on the roadway adjacent to a right side of the vehicle12where the vehicle12may legally travel if no other vehicles are occupying the right adjacent lane of travel adjacent to the vehicle12. In an exemplary embodiment the controller14identifies the state of the right adjacent lane of travel using the at least one vehicle sensor16. The state of the right adjacent lane of travel includes either an unoccupied state (i.e., no other vehicle is occupying an area in the right adjacent lane adjacent to the vehicle12) or an occupied state (i.e., another vehicle is occupying the area in the right adjacent lane adjacent to the vehicle12). If the state of the right adjacent lane of travel is unoccupied, the exemplary embodiment of block116proceeds to block116f. If the state of the right adjacent lane of travel is occupied, the exemplary embodiment of block116proceeds to block116g.

At block116f, the controller14commands the vehicle12to move into the right adjacent lane of travel such that the vehicle12is no longer directly in front of the remote vehicle34. After block116f, the exemplary embodiment of block116is concluded and the method100continues.

At block116g, the controller14identifies a state of a left adjacent lane of travel. In an exemplary embodiment, the left adjacent lane of travel is a lane on the roadway adjacent to a left side of the vehicle12where the vehicle12may legally travel if no other vehicles are occupying the left adjacent lane of travel adjacent to the vehicle12. In an exemplary embodiment the controller14identifies the state of the left adjacent lane of travel using the at least one vehicle sensor16. The state of the left adjacent lane of travel includes either an unoccupied state (i.e., no other vehicle is occupying an area in the left adjacent lane adjacent to the vehicle12) or an occupied state (i.e., another vehicle is occupying the area in the left adjacent lane adjacent to the vehicle12). If the state of the left adjacent lane of travel is unoccupied, the exemplary embodiment of block116proceeds to block116h. If the state of the left adjacent lane of travel is occupied, the exemplary embodiment of block116proceeds to block116i.

At block116h, the controller14commands the vehicle12to move into the left adjacent lane of travel such that the vehicle12is no longer directly in front of the remote vehicle34. After block116h, the exemplary embodiment of block116is concluded and the method100continues.

At block116i, the controller14determines a chase time of the remote vehicle34. In the scope of the present disclosure, the chase time is an amount of time which the remote vehicle34has been tailgating (i.e., following closely) the vehicle12. In an exemplary embodiment, the chase time is determined using the at least one vehicle sensor16to detect and track the remote vehicle34. If the chase time is greater than or equal to a predetermined chase time threshold (e.g., five minutes), the exemplary embodiment of block116proceeds to block116j. If the chase time is less than the predetermined chase time threshold, the exemplary embodiment of block116returns to block116e.

At block116j, the controller14notifies the occupant of the vehicle12of the tailgating remote vehicle34using the display22. In an exemplary embodiment, the occupant of the vehicle12is notified with safety advice, such as, for example, to avoid eye contact with an occupant of the remote vehicle34, such that the occupant of the remote vehicle34is not provoked to further action. After block116j, the exemplary embodiment of block116proceeds to block116k.

At block116k, the vehicle12pulls over to a shoulder of the roadway. In an exemplary embodiment, the controller14additionally uses the vehicle communication system20to transmit a report of the tailgating behavior of the remote vehicle34to other vehicles and/or infrastructure. In another exemplary embodiment, the controller14uses the vehicle communication system20to submit a report identifying the remote vehicle34to a public safety authority (e.g., a police department). After block116k, the exemplary embodiment of block116is concluded and the method100continues.

Referring toFIG.6, a flowchart of an exemplary embodiment of the block118discussed above is provided. The exemplary embodiment of block118is performed when a determination is made that the remote vehicle34is exhibiting road rage behavior toward the vehicle12. The exemplary embodiment of block118begins at block118a. At block118a, the controller14notifies the occupant of the vehicle12of the road rage behavior of the remote vehicle34using the display22. In an exemplary embodiment, the occupant of the vehicle12is notified to avoid eye contact and obscene gestures with an occupant of the remote vehicle34, such that the occupant of the remote vehicle34is not provoked to further action. After block118a, the exemplary embodiment of block118proceeds to block118b.

At block118b, a lane state of the remote vehicle34is determined. In the scope of the present disclosure, the lane state of the remote vehicle34identifies whether the remote vehicle34is traveling in a same lane of the roadway relative to the vehicle12(i.e., a same lane state) or whether the remote vehicle34is traveling in an adjacent lane of the roadway relative to the vehicle12(i.e., an adjacent lane state). In an exemplary embodiment, the lane state is determined using the at least one vehicle sensor16to track a position of the remote vehicle34relative to the vehicle12. If the lane state is the same lane state, the exemplary embodiment of block118proceeds to block118c. If the lane state is the adjacent lane state, the exemplary embodiment of block118proceeds to block118d.

At block118c, the controller14commands the vehicle12to move into an adjacent lane of travel such that the vehicle12is no longer directly in front of the remote vehicle34. After block118c, the exemplary embodiment of block118proceeds to block118d.

At block118d, the controller14commands the vehicle12to reduce the speed of the vehicle12, such that the remote vehicle34may pass the vehicle12. In an exemplary embodiment, the speed of the vehicle12is decreased by ten miles per hour. After block118d, the exemplary embodiment of block118proceeds to blocks118eand118f.

At block118e, the controller14identifies whether the remote vehicle34has passed the vehicle12. In an exemplary embodiment, to identify whether the remote vehicle34has passed the vehicle12, the controller14identifies a relative location of the vehicle12to the remote vehicle34. The relative location may be a leading relative location (i.e., the vehicle12is in front of the remote vehicle34) or a following relative location (i.e., the vehicle12is behind the remote vehicle34). In an exemplary embodiment, the relative location is identified using the at least one vehicle sensor16. If the relative location of the vehicle12is the following relative location, the exemplary embodiment of block118is concluded and the method100continues. If the relative location of the vehicle12is the leading relative location (i.e., the remote vehicle34has refused to pass the vehicle12), the exemplary embodiment of block118proceeds to block118g.

At block118g, the controller14executes a first evasive action. In an exemplary embodiment, the first evasive action includes commands to lock all doors of the vehicle12and close all windows of the vehicle12. In another exemplary embodiment, the first evasive action includes commands to execute instructions to contact the public safety authority (e.g., the police department) and change a navigation destination of the vehicle12to a public location (e.g., a public parking lot, a police station, or the like). In another exemplary embodiment, the first evasive action further includes commands to stop the vehicle12at the public location. After block118g, the exemplary embodiment of block118is concluded and the method100continues.

At block118f, the controller14determines whether the occupant of the remote vehicle34has exited the remote vehicle34. In an exemplary embodiment, the controller14uses the at least one vehicle sensor16to determine whether the occupant of the remote vehicle34has exited the remote vehicle34. If the occupant of the remote vehicle34has exited the remote vehicle34, the exemplary embodiment of block118proceeds to block118h. If the occupant of the remote vehicle34has not exited the remote vehicle34, the exemplary embodiment of block118returns to blocks118eand118f.

At block118h, the controller14executes a second evasive action. In an exemplary embodiment, the second evasive action includes commands to activate a horn of the vehicle12repeatedly and collect identifying information about the occupant of the remote vehicle34using the at least one vehicle sensor16. In a non-limiting example where the at least one vehicle sensor16includes a camera, the camera is used to record photos and/or videos of the occupant of the remote vehicle34and/or the remote vehicle34. After block118h, the exemplary embodiment of block118is concluded and the method100continues.

Referring toFIG.7, a flowchart of an exemplary embodiment of the block120discussed above is provided. The exemplary embodiment of block118is performed when a determination is made that the remote vehicle34is traveling in a wrong direction. The exemplary embodiment of block120begins at block120a. At block120a, a predicted path of the remote vehicle34is identified. In an exemplary embodiment, if the remote vehicle34is not detected by the at least one vehicle sensor16, the controller14uses the vehicle communication system20to communicate with the remote vehicle34and determine a location, speed, and heading of the remote vehicle34to identify the predicted path of the remote vehicle34. In a non-limiting example, the location, speed, and heading of the remote vehicle34are transmitted by the remote vehicle34to the vehicle communication system20. In another non-limiting example, the vehicle communication system20is used to communicate with infrastructure (e.g., an electronic road sign). The infrastructure provides information about the remote vehicle34used by the controller14to determine the predicted path of the remote vehicle34. In yet another non-limiting example, the at least one vehicle sensor16is used to capture an image of an electronic road sign displaying a warning message and use an optical character recognition algorithm to retrieve information about the predicted path of the remote vehicle34based on the image of the electronic road sign. In another exemplary embodiment, if the remote vehicle34is detected by the at least one vehicle sensor16, the controller14uses the at least one vehicle sensor16to identify the location, speed, and heading of the remote vehicle34to identify the predicted path of the remote vehicle34. After block120a, the exemplary embodiment of block120proceeds to block120b.

At block120b, the predicted path of the remote vehicle34is classified as either a collision path or a non-collision path. In an exemplary embodiment, the predicted path is classified based on a location of the vehicle12(determined using the GNSS18) and the predicted path determined at block120a. If the predicted path of the remote vehicle34is classified as the collision path, the exemplary embodiment of block120proceeds to block120c. If the predicted path of the remote vehicle34is classified as the non-collision path, the exemplary embodiment of block120proceeds to block120d.

At block120c, the controller14commands the vehicle12to move into a shoulder of the roadway to avoid a collision with the remote vehicle34. In an exemplary embodiment, the controller14additionally limits a braking ability of the vehicle12, such that the vehicle12may quickly move out of a collision path with the remote vehicle34. After block120c, the exemplary embodiment of block120is concluded and the method100continues.

At block120d, the controller14takes a third evasive action. In an exemplary embodiment, the third evasive action includes commands to activate the horn of the vehicle12repeatedly, flash indicator lights of the vehicle12repeatedly, increase a following distance between the vehicle12and any other vehicles on the roadway, reduce a speed of the vehicle12, change a navigation destination of the vehicle12to exit the roadway, communicate information about the remote vehicle34to other vehicles using the vehicle communication system20, and communicate identifying information about the remote vehicle34to a public safety authority (e.g., police) using the vehicle communication system20. After block120d, the exemplary embodiment of block120is concluded and the method100continues.

Referring toFIGS.8aand8b, a second exemplary driving environment30band a third exemplary driving environment30care shown to further illustrate the operation of the system10and method100. Referring toFIG.8a, the second exemplary driving environment30bis shown. In the second exemplary driving environment30b, the remote vehicle34reduces speed often and drifts towards a left side of second exemplary driving environment30b(e.g., a roadway) often. Therefore, the path determination machine learning algorithm may decide to overtake the remote vehicle34. Referring toFIG.8b, the third exemplary driving environment30cis shown. In the third exemplary driving environment30c, the remote vehicle34is driving at a high rate of speed. Therefore, the path determination machine learning algorithm may decide to remain in an adjacent lane relative to the remote vehicle34, allowing the remote vehicle34to pass.

The description of the present disclosure is merely exemplary in nature and variations that do not depart from the gist of the present disclosure are intended to be within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure.