Abstract: In exemplary embodiments, methods and systems are provided that include one or more sensors of a vehicle that are configured to obtain sensor data as to operation of the vehicle; a satellite-based location system of the vehicle that is configured to obtain location data as to a geographic location of the vehicle; and a processor of the vehicle that is coupled to the one or more sensors and to the location system and that is configured to at least facilitate determining, using the sensor data and the location data, a maneuver for the vehicle based on an intent of a driver of the vehicle in initiating the maneuver; and characterizing one or more parameters as to an expected behavior of the vehicle based on the maneuver, utilizing a mathematical procedure including a probability function.

Abstract: A computer-implemented method that, when executed by data processing hardware, causes the data processing hardware to perform operations comprising detecting a request to enable an automated driving system of a vehicle while the vehicle is at rest or at a low speed, determining an intended path of the vehicle, determining an initial steering angle, calculating a desired steering angle based on the intended path, calculating a commanded steering angle, evaluating the initial steering angle versus the desired steering angle, and either (i) initiating a driving maneuver with the initial steering angle, or (ii) adjusting the steering angle based on the commanded steering angle before initiating the driving maneuver.

Abstract: A vehicle system includes a control module configured to receive data from a sensor indicative of a torque applied to the steering wheel, calculate a torque rate based on the applied torque, calculate a left evasive steering probability of the driver intending to steer the vehicle to the left and a right evasive steering probability of the driver intending to steer the vehicle to the right based on the torque, the torque rate, and a modeled relationship between the torque and the torque rate, compare the left evasive steering probability and the right evasive steering probability to determine a directional steering intent of the driver, and transmit a signal to a threat avoidance steering control module for controlling the vehicle. Other example vehicle systems and methods for predicting driver intent in evasive steering maneuvers are also disclosed.

Abstract: A system operates a method for steering a vehicle. A sensor obtains a first stream of data related to road wheel angle for the vehicle and a second stream of data related to lateral acceleration for the vehicle. A processor determines a reduced tire model for the vehicle using the first stream of data and the second stream of data, obtains a measurement of a current road wheel angle and a measurement of a current lateral acceleration, determines a current slope from the current road wheel angle and the current lateral acceleration, compares the current slope to the reduced tire model to predict a level of saturation of a tire of the vehicle, and controls a steering actuator of the vehicle to steer the vehicle based on the level of saturation of the tire.

Abstract: A vehicle includes a plurality of sensors and one or more controllers collectively programmed to execute the following instructions: estimate odometry of the vehicle based on sensor data from the plurality of sensors, to produce a plurality of odometry estimates; monitor integrity of one or more of the sensors; correct sensor data from one or more of the plurality of sensors to provide corrected sensor data; compensate the corrected sensor data to provide compensated data; and use the compensated data to provide at least one of vehicle odometry, vehicle states, and time stamp information.

Abstract: A vehicle includes a plurality of sensors and one or more controllers collectively programmed to execute the following instructions: estimate odometry of the vehicle based on sensor data from the plurality of sensors, to produce a plurality of odometry estimates; monitor integrity of one or more of the sensors; correct sensor data from one or more of the plurality of sensors to provide corrected sensor data; compensate the corrected sensor data to provide compensated data; and use the compensated data to provide at least one of vehicle odometry, vehicle states, and time stamp information.

Abstract: A system includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that, when executed on the data processing hardware cause the data processing hardware to perform operations. The operations include monitoring, via a trajectory control architecture of a controller, at least one active safety feature and a trajectory of a vehicle, tracking, via the trajectory control architecture, an error of the trajectory, and comparing the tracked error with a trajectory history stored on the controller. The operations also include adapting, based on the comparison of the tracked error and the trajectory history, performance elements of the vehicle via the trajectory control architecture and monitoring, based on the adapted performance elements, the trajectory of the vehicle.

Abstract: A system and method are provided for controlling a vehicle. In one embodiment, a system includes: a sensor system configured to generate sensor data sensed from an environment of the vehicle; and a control module configured to, by a processor, based on the sensor data, identify a gap in a lane line on a roadway in front of the vehicle, determine at least two points within the gap, determine a curve between the at least two points, compute a correlation measure based on the curve, generate lane stitching data based on data based on the curve and an evaluation of the correlation measure, and controlling one or more components of the vehicle based on the stitching data.

Abstract: A vehicle includes a system for calibrating a sensor of the vehicle moving on a road section. The processor determines a control condition variable indicative of a trajectory of the vehicle with respect to a straight line, determines a bank angle condition variable indicative of a motion of the vehicle with respect to the straight line based on a bank angle of the road section, determines a torque condition variable indicative of the motion of the vehicle with respect to the straight line based on a torque applied to a steering wheel of the vehicle, determines a straight-line condition for the vehicle when at least one of the control condition variable, the bank angle condition variable and the torque condition variable indicates that the vehicle is moving in the straight line, and calibrates the sensor when the vehicle is in the straight-line condition.

Abstract: A computer-implemented method that, when executed by data processing hardware, causes the data processing hardware to perform operations comprising detecting a request to enable an automated driving system of a vehicle while the vehicle is at rest or at a low speed, determining an intended path of the vehicle, determining an initial steering angle, calculating a desired steering angle based on the intended path, calculating a commanded steering angle, evaluating the initial steering angle versus the desired steering angle, and either (i) initiating a driving maneuver with the initial steering angle, or (ii) adjusting the steering angle based on the commanded steering angle before initiating the driving maneuver.

Abstract: Vehicles and related systems and methods are provided for predicting a future maneuver expected to be executed by a driver. One method involves determining a combined probability for a potential maneuver based on constituent probabilities corresponding to different vehicle state variables, and environmental conditions using one or more tuning parameters specific to the respective combination of maneuver and vehicle state variable, determining an adjusted probability for the potential maneuver based at least in part on the combined probability and one or more contextual state variables corresponding to a current operating context for the vehicle, adapted to driver behavior and driving style, identifying the potential maneuver as an expected maneuver to be executed by a driver of the vehicle based on the adjusted probability, and automatically initiating one or more actions in a manner that is influenced by the expected maneuver.

Abstract: Noise filtering for a wheel speed or other sensor, such as a wheel speed sensor configured for sensing rotational speed of a wheel included onboard a vehicle. A filtering process may include determining sensor data generated with the wheel speed sensor, the sensor data representing rotational speed of the wheel, determining a steady interval of wheel rotation and a non-steady interval of wheel rotation, and filtering noise from the sensor data associated with the steady interval according to an adaptive filtering process.

Abstract: A vehicle system includes a control module configured to receive data from a sensor indicative of a torque applied to the steering wheel, calculate a torque rate based on the applied torque, calculate a left evasive steering probability of the driver intending to steer the vehicle to the left and a right evasive steering probability of the driver intending to steer the vehicle to the right based on the torque, the torque rate, and a modeled relationship between the torque and the torque rate, compare the left evasive steering probability and the right evasive steering probability to determine a directional steering intent of the driver, and transmit a signal to a threat avoidance steering control module for controlling the vehicle. Other example vehicle systems and methods for predicting driver intent in evasive steering maneuvers are also disclosed.

Abstract: Vehicles and related systems and methods are provided for predicting a future maneuver expected to be executed by a driver. One method involves determining a combined probability for a potential maneuver based on constituent probabilities corresponding to different vehicle state variables, and environmental conditions using one or more tuning parameters specific to the respective combination of maneuver and vehicle state variable, determining an adjusted probability for the potential maneuver based at least in part on the combined probability and one or more contextual state variables corresponding to a current operating context for the vehicle, adapted to driver behavior and driving style, identifying the potential maneuver as an expected maneuver to be executed by a driver of the vehicle based on the adjusted probability, and automatically initiating one or more actions in a manner that is influenced by the expected maneuver.

Abstract: A system and method are provided for controlling a vehicle. In one embodiment, a system includes: a sensor system configured to generate sensor data sensed from an environment of the vehicle; and a control module configured to, by a processor, based on the sensor data, identify a gap in a lane line on a roadway in front of the vehicle, determine at least two points within the gap, determine a curve between the at least two points, compute a correlation measure based on the curve, generate lane stitching data based on data based on the curve and an evaluation of the correlation measure, and controlling one or more components of the vehicle based on the stitching data.

Abstract: A RLP system for a host vehicle includes a memory and levels. The memory stores a RLP algorithm, which is a multi-agent collaborative DQN with PER algorithm. A first level includes a data processing module that provides sensor data, object location data, and state information of the host vehicle and other vehicles. A second level includes a coordinate location module that, based on the sensor data, the object location data, the state information, and a refined policy provided by the third level, generates an updated policy and a set of future coordinate locations implemented via the first level. A third level includes evaluation and target neural networks and a processor that executes instructions of the RLP algorithm for collaborative action planning between the host and other vehicles based on outputs of the evaluation and target networks and to generate the refined policy based on reward values associated with events.