Abstract: A method for controlling an autonomous vehicle includes receiving road data. The road data includes information about a plurality of potential events along the road ahead of the autonomous vehicle. The method further includes determining, in real time, a probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur while the autonomous vehicle moves along the road and determining, in real time, an adjusted planned path using a probabilistic predictive control that takes into account the probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur. Further, the method includes controlling the autonomous vehicle to cause the autonomous vehicle to autonomously follow the adjusted planned path.

Abstract: A method for controlling a steer-by-wire system, comprising receiving vehicle data and a steering request from a vehicle, determining whether a steering road wheel actuator of the vehicle has failed using the vehicle data, in response to determining that the steering road wheel actuator of the vehicle has failed, determining a target wheel slip of the vehicle based on the steering request, maintaining the target wheel slip of the vehicle while the vehicle is in motion; and adjusting a wheel speed of at least one wheel of the vehicle based on a feedback signal, wherein the feedback signal is indicative of a road wheel angle while the vehicle is in motion.

Abstract: A fault remediation system for a vehicle includes one or more controllers in electronic communication with one or more consumed interfaces and one or more provided interfaces. The one or more controllers execute instructions to receive, from the one or more consumed interfaces, a consumed signal and perform fault detection upon the consumed signal to determine the presence of an active fault within the consumed signal. In response to detecting an active fault with the consumed signal, the one or more controllers select a remediation state from a group of two or more prospective remediation states based on a significance analysis of the consumed signal. The one or more controllers evaluate a relevant subfunction that corresponds to the consumed signal that the remediation state addresses for the presence of remediation tolerance and generates arbitration instructions based on the remediation tolerance.

Abstract: A method for downforce control includes receiving sensor data from sensors, using a feedforward control to determine a first requested normal force at the first axle of the vehicle and a second requested normal force at the second axle of the vehicle and the sensor data, using a feedback control to determine a first requested normal force adjustment at the first axle of the vehicle and a second requested normal force adjustment at the second axle of the vehicle using the sensor data, fusing the first requested normal force at the first axle of the vehicle with the first requested normal force adjustment to determine a first-adjusted normal force request at the first axle, and fusing the second requested normal force with the second requested normal force adjustment to determine a second-adjusted normal force request at the second axle.

Abstract: A method for downforce control includes receiving a plurality of vehicle inputs from a vehicle. The plurality of vehicle inputs includes sensor data from a plurality of sensors of the vehicle. The method further includes determining a downforce acting on the vehicle, by: (a) determining a predicted half-car model uncertainties using a neural network; and (b) determining a front normal force at the front axle and a rear normal force at the rear axle using the vehicle inputs, the predicted half-car model uncertainties, and a half-car model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the front normal force at the front axle and a rear normal force at the rear axle, respectively.

Abstract: A method for downforce control includes receiving vehicle inputs. The method includes determining a first normal-force request at the front axle and a second normal-force request at the rear axle using the purality of vehicle inputs and a prediction model. The prediction model is a combined state space model that integrates a half-car state space model and an actuator state space model, the half-car state space model is developed using a half-car model, and the actuator state space model is developed using a neural network model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the first normal-force request and the second normal-force request, respectively.

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 method for vehicle motion control includes receiving sensor data from a plurality of sensors of a vehicle and monitoring a vehicle response of the vehicle using the sensor data. The vehicle response is represented by a plurality of vehicle-response signals. The method further includes fusing the plurality of vehicle-response signals to obtain at least one fused signal. The method further includes determining whether to activate a vehicle stability control of the vehicle based on the at least one fused signal and commanding the vehicle to activate the vehicle stability control in response to determining to activate the vehicle stability control of the vehicle based on the at least one fused signal.

Abstract: A method for data driven downforce control of a vehicle includes receiving a first requested downforce at the front axle of a vehicle and a second requested downforce at the rear axle of the vehicle. The method further includes using a model-based control to determine a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the first requested downforce and the second requested downforce. The model-based control is based on a predetermined aerodynamic map. The method includes commanding the first aerodynamic actuator to move the first aerodynamic body to the first position. The method includes commanding the second aerodynamic actuator to move the second aerodynamic body to the second position.

Abstract: A method for controlling an autonomous vehicle includes receiving road data. The road data includes information about a plurality of potential events along the road ahead of the autonomous vehicle. The method further includes determining, in real time, a probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur while the autonomous vehicle moves along the road and determining, in real time, an adjusted planned path using a probabilistic predictive control that takes into account the probability that the plurality of potential events along the road ahead of the autonomous vehicle will occur. Further, the method includes controlling the autonomous vehicle to cause the autonomous vehicle to autonomously follow the adjusted planned path.

Abstract: A fault remediation system for a vehicle includes one or more controllers in electronic communication with one or more consumed interfaces and one or more provided interfaces. The one or more controllers execute instructions to receive, from the one or more consumed interfaces, a consumed signal and perform fault detection upon the consumed signal to determine the presence of an active fault within the consumed signal. In response to detecting an active fault with the consumed signal, the one or more controllers select a remediation state from a group of two or more prospective remediation states based on a significance analysis of the consumed signal. The one or more controllers evaluate a relevant subfunction that corresponds to the consumed signal that the remediation state addresses for the presence of remediation tolerance and generates arbitration instructions based on the remediation tolerance.

Abstract: A driver command predictor includes a controller, multiple sensors, and a command prediction unit. The controller is configured to command an adjustment of multiple motion vectors of a vehicle relative to a roadway in response to multiple actual driver commands and multiple future driver commands. The actual driver commands are received at a current time. The future driver commands are received at multiple update times. The update times range from the current time to a future time. The sensors are configured to generate sensor data that determines multiple actual states of the vehicle in response to the motion vectors as commanded. The command prediction unit is configured to generate the future driver commands at the update times in response to a driver model. The driver model operates on the actual driver commands and the actual states to predict the future driver commands at the update times.

Abstract: A vehicle motion control system includes one or more input devices for generating one or more input signals associated with data indicative of a motion of the vehicle. The system further includes a computer, which has one or more processors. The computer further includes a non-transitory computer readable storage medium for storing instructions, such that the processor is programmed to compare a current tire state and a current tire prediction model to the data indicative of the motion of the vehicle. The processor is further programmed to calculate in real-time an adjusted tire state and an adjusted tire prediction model. The processor is further programmed to generate in real-time one or more actuation signals based on the adjusted tire state and the adjusted tire prediction model. The actuators in real-time adjust the motion of the vehicle in response to the actuator receiving the actuation signal from the processor.

Abstract: A method for vehicle motion control includes receiving sensor data from a plurality of sensors of a vehicle and monitoring a vehicle response of the vehicle using the sensor data. The vehicle response is represented by a plurality of vehicle-response signals. The method further includes fusing the plurality of vehicle-response signals to obtain at least one fused signal. The method further includes determining whether to activate a vehicle stability control of the vehicle based on the at least one fused signal and commanding the vehicle to activate the vehicle stability control in response to determining to activate the vehicle stability control of the vehicle based on the at least one fused signal.

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 vehicle motion control are provided. The method includes: calculating a correction factor using one of three different sets of operations when the vehicle is performing a limit handling maneuver, wherein the correction factor is calculated using a first set of operations when the vehicle is operating in an understeer state, calculated using a second set of operations when the vehicle is operating in an oversteer state, and calculated using a third set of operations when the vehicle is operating in a neutral steer state; adjusting a desired lateral acceleration and a desired yaw rate by applying the correction factor to account for a reduced level of friction experienced by the vehicle when traveling on a non-ideal friction surface; calculating optimal control actions based on the adjusted desired lateral acceleration and adjusted desired yaw rate; and applying the optimal control actions with vehicle actuators during vehicle operations.

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 method of controlling a vehicle includes obtaining a linear representation of a vehicle dynamics model that includes actuator dynamics u integrated with vehicle dynamics x. The actuator dynamics u include a road wheel angle at rear wheels ?r and a torque Mz. The method also includes obtaining an objective function based on a function of the vehicle dynamics x and the actuator dynamics u and formulating a cost function to minimize the objective function. The actuator dynamics u including the torque Mz are determined for a next time sample based on minimizing the objective function. The vehicle is controlled to implement the torque Mz.

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 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.