Abstract: A method includes receiving planned path data, road data, and speed profile data, determining anticipatory constraints for each of the plurality of steps along a planned path using the planned path data, the road data, and the speed profile data, determining a plurality of control actions using a Model Predictive Control (MPC). The prediction model of the MPC is updated in real time with the plurality of anticipatory constraints for each of the plurality of steps along the planned path and the road data for each of the plurality of steps along the planned path. The method further includes controlling the vehicle using the plurality of control actions to cause the vehicle to autonomously follow the planned path.

Abstract: A method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.

Abstract: A method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.

Abstract: A trajectory planning system for an autonomous vehicle includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle. The one or more controllers determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver. The one or more controllers select a trajectory from the plurality of relative state trajectories for the autonomous vehicle, where the autonomous vehicle follows the trajectory while performing the maneuver.

Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to generate vehicle-level commands based on received vehicle operation commands. The received vehicle operation commands can comprise input commands corresponding to at least one of an autonomous vehicle (AV) mode of operation or a manual mode of operation. The processor is also programmed to generate target actuator commands based on the vehicle-level commands and transmit the target actuator commands to at least one actuator.

Abstract: A system includes a primary control module, a stability status module, and a supervisory control module. The primary control module is configured to determine at least one control action for at least one of an electronic limited slip differential and an aerodynamic actuator of a vehicle based on a driver command. The stability status module is configured to determine whether at least one component of the vehicle is stable or unstable based on an input from a sensor on the vehicle. The at least one component includes at least one of a vehicle body, a front axle, a rear axle, front wheels, and rear wheels. The supervisory control module is configured to adjust the at least one control action when the at least one component is unstable.

Abstract: A system for supervisory control for eAWD and eLSD in a motor vehicle includes a control module, and sensors and actuators disposed on the motor vehicle. The sensors measure real-time motor vehicle data, and the actuators alter behavior of the motor vehicle. The control module receives the real-time data; receives one or more driver inputs to the motor vehicle; determines a status of a body of the motor vehicle; determines a status of axles of the motor vehicle; determines a status of each wheel of the motor vehicle; and generates a control signal to the actuators from the driver inputs and the body, axle, and wheel statuses. The control module also exercises supervisory control by actively adjusting constraints on the control signal to each of the actuators where actively adjusting constraints on the control signal alters boundaries of control actions in response to the one or more driver inputs.

Abstract: Systems and methods for controlling a vehicle are provided. The systems and methods include a sensor system and a processor configured to execute program instructions, to cause the at least one processor to: receive yaw rate values, lateral acceleration values and longitudinal velocity values for the vehicle from the sensor system, determine side slip angle parameter values based on the yaw rate values, lateral acceleration values and longitudinal velocity values, determine phase portrait angles based on the side slip angle parameter values and the yaw rate values, wherein the phase portrait angles each represent an angle between yaw rate and side slip angle for the vehicle in a phase portrait of yaw rate and side slip angle, detect or predict vehicle instability based at least on the phase portrait angles, and when vehicle instability is detected or predicted, control motion of the vehicle to at least partly correct the vehicle instability.

Abstract: A control allocation system for a vehicle includes an electric power steering (EPS) system, one or more redundant actuation systems for controlling a plurality of wheels of the vehicle, and one or more controllers in electronic communication with the EPS system and the one or more redundant actuation systems. The one or more controllers execute instructions to determine tracking errors and vehicle dynamics states based on a plurality of local path planning references and receive a fault signal indicating the EPS system is non-functional. In response to receiving the fault signal, the one or more controllers determine a plurality of corrective constraints in real-time. The one or more controllers solve a real-time constrained optimization problem for each sampling interval of the control allocation system to determine a plurality of control actions based on the plurality of corrective constraints and the tracking errors.

Abstract: A control allocation system for a vehicle includes an electric power steering (EPS) system, one or more redundant actuation systems for controlling a plurality of wheels of the vehicle, and one or more controllers in electronic communication with the EPS system and the one or more redundant actuation systems. The one or more controllers execute instructions to determine tracking errors and vehicle dynamics states based on a plurality of local path planning references and receive a fault signal indicating the EPS system is non-functional. In response to receiving the fault signal, the one or more controllers determine a plurality of corrective constraints in real-time. The one or more controllers solve a real-time constrained optimization problem for each sampling interval of the control allocation system to determine a plurality of control actions based on the plurality of corrective constraints and the tracking errors.

Abstract: A system for supervisory control for eAWD and eLSD in a motor vehicle includes a control module, and sensors and actuators disposed on the motor vehicle. The sensors measure real-time motor vehicle data, and the actuators alter behavior of the motor vehicle. The control module receives the real-time data; receives one or more driver inputs to the motor vehicle; determines a status of a body of the motor vehicle; determines a status of axles of the motor vehicle; determines a status of each wheel of the motor vehicle; and generates a control signal to the actuators from the driver inputs and the body, axle, and wheel statuses. The control module also exercises supervisory control by actively adjusting constraints on the control signal to each of the actuators where actively adjusting constraints on the control signal alters boundaries of control actions in response to the one or more driver inputs.

Abstract: A system includes a clutch state module and a clutch torque module. The clutch state module is configured to determine whether a clutch of an electronic limited slip differential is locked or unlocked. The electronic limited slip differential couples an engine of a vehicle to left and right wheels of the vehicle. The clutch torque module is configured to estimate an actual torque transferred by the clutch using a first clutch torque model when the electronic limited slip differential is unlocked, and estimate the actual clutch torque using a second clutch torque model when the electronic limited slip differential is locked. The second clutch torque model is different than the first clutch torque model.

Abstract: A system for managing chassis and driveline actuators of a motor vehicle includes a control module executing program code portions that: cause sensors to obtain vehicle state information, receive a driver input and generate a desired dynamic output based on the driver input and the vehicle state information, and then estimate actuator actions based on the vehicle state information, generate one or more control action constraints based on the vehicle state information and estimated actuator actions, generate a reference control action based on the vehicle state information, the estimated actions of the one or more actuators and the control action constraints, and integrate the vehicle state information, the estimated actuator actions, desired dynamic output, reference control action and the control action constraints to generate an optimal control action that falls within a range of predefined actuator capacities and ensures driver control of the vehicle.

Abstract: A system and method of automatically activating a windshield wiper system of a vehicle having a front windshield with a front wiper and a rear windshield with a rear wiper are provided. The method comprises assessing at least one windshield classification of road conditions based on original information and capturing a front image of the front windshield, a rear image of the rear windshield, and an environment image of the environment. The method further comprises classifying the images to define a first windshield class. The method further comprises determining a front windshield perception, a rear windshield perception, and an environment perception of the first windshield class to define a first combination of detection sources. The method further comprises fusing the front windshield perception, the rear windshield perception and the environment perception, defining a first front probability of the first windshield class.

Abstract: Systems and methods for controlling a vehicle are provided. The systems and methods include a sensor system and a processor configured to execute program instructions, to cause the at least one processor to: receive yaw rate values, lateral acceleration values and longitudinal velocity values for the vehicle from the sensor system, determine side slip angle parameter values based on the yaw rate values, lateral acceleration values and longitudinal velocity values, determine phase portrait angles based on the side slip angle parameter values and the yaw rate values, wherein the phase portrait angles each represent an angle between yaw rate and side slip angle for the vehicle in a phase portrait of yaw rate and side slip angle, detect or predict vehicle instability based at least on the phase portrait angles, and when vehicle instability is detected or predicted, control motion of the vehicle to at least partly correct the vehicle instability.

Abstract: A system includes a primary control module, a stability status module, and a supervisory control module. The primary control module is configured to determine at least one control action for at least one of an electronic limited slip differential and an aerodynamic actuator of a vehicle based on a driver command. The stability status module is configured to determine whether at least one component of the vehicle is stable or unstable based on an input from a sensor on the vehicle. The at least one component includes at least one of a vehicle body, a front axle, a rear axle, front wheels, and rear wheels. The supervisory control module is configured to adjust the at least one control action when the at least one component is unstable.

Abstract: A system includes a clutch state module and a clutch torque module. The clutch state module is configured to determine whether a clutch of an electronic limited slip differential is locked or unlocked. The electronic limited slip differential couples an engine of a vehicle to left and right wheels of the vehicle. The clutch torque module is configured to estimate an actual torque transferred by the clutch using a first clutch torque model when the electronic limited slip differential is unlocked, and estimate the actual clutch torque using a second clutch torque model when the electronic limited slip differential is locked. The second clutch torque model is different than the first clutch torque model.

Abstract: A traction control module includes a sensor/estimation module configured to output wheel stability data based on a plurality of wheel condition inputs and a wheel stability monitoring module configured to calculate a plurality of wheel stability predictors based on the wheel stability data. Each of the wheel stability predictors is independently indicative of a wheel slip condition. The traction control module further includes a wheel stability data fusion module configured to receive each of the plurality of wheel stability predictors, combine selected wheel stability predictors from the plurality of wheel stability predictors to generate combinations of the wheel stability predictors, and selectively output a torque reduction request based on the combinations of the wheel stability predictors.

Abstract: Methods of controlling axle torque distribution of a vehicle during steering through a curve include collecting, via a controller: input data which is representative of a plurality of vehicle inputs; vehicle data which is representative of axle torque of the front axle and axle torque of the rear axle; and constraint data which is representative of real-time constraints of the vehicle. The collected input data, vehicle data and constraint data are communicated to a predictive model. Determining, using the predictive model, whether torque adjustments are necessary. The distribution of the axle torque of the front axle and the axle torque of the rear axle is controlled, via the controller, when the torque adjustments are necessary as determined via the predictive model.

Abstract: Methods of controlling axle torque distribution of a vehicle during steering through a curve include collecting, via a controller: input data which is representative of a plurality of vehicle inputs; vehicle data which is representative of axle torque of the front axle and axle torque of the rear axle; and constraint data which is representative of real-time constraints of the vehicle. The collected input data, vehicle data and constraint data are communicated to a predictive model. Determining, using the predictive model, whether torque adjustments are necessary. The distribution of the axle torque of the front axle and the axle torque of the rear axle is controlled, via the controller, when the torque adjustments are necessary as determined via the predictive model.