Abstract: A motor vehicle motion control health monitoring system includes sensors and actuators disposed on the motor vehicle. The sensors measure real-time static and dynamic telemetry data about the motor vehicle, and the actuators alter static and dynamic behavior of the motor vehicle. A control module has a processor, a memory, and input/output (I/O) ports. The processor executes program code portions stored in the memory, the program code portions include: an offline portion that collects telemetry data from the motor vehicle, performs failure analysis on the telemetry data and allocates tasks based on the failure analysis; and an online portion that analyzes the telemetry data for failures within specific sensors, actuators, or functions that utilize systems of sensors and/or actuators. The online portion mitigates deviations in the telemetry data by sending a correction to the one or more sensors, actuators, and/or functions of a motor vehicle motion control system.

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 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 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 rule-based adaptive cruise control system for determining a vehicle-following behavior of a vehicle includes a plurality of perception sensors for collecting perception data representing an environment surrounding the vehicle and one or more controllers in electronic communication with the plurality of perception sensors. The one or more controllers categorize a profile of a roadway based on a route plan and the perception data into one of a plurality of roadway profiles that are each indicative of the geometry of the roadway, categorize detection of a preceding vehicle based on the perception data into one of a plurality of detection states, classify the selected roadway profile and the selected detection state into one of a plurality of scenarios based on a set of classification rules, and assign a specific vehicle-following behavior based on the selected scenario.

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 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 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 computing system including a cooperative system architecture for cataloging, informing, sharing, and managing engineering assets within an organization includes a user management subsystem for creating a registered user profile associated with a specific user of the computing system. The registered user profile includes user metadata that provides identifying characteristics of the specific user. The computing system includes an asset management subsystem including an asset repository for recording one or more engineering assets. The asset management subsystem modifies the user metadata of the registered user profile in response to the specific user recording an engineering asset in the asset repository. The computing system includes a test management subsystem including a test repository for recording one or more test bill of materials.

Abstract: A system for real-time control selection and calibration in a vehicle using a deep-Q network (DQN) includes sensors and actuators disposed on the vehicle. A control module has a processor, memory, and input/output (I/O) ports in communication with the one or more sensors and the one or more actuators. The processor executes program code portions that cause the sensors actuators to obtain vehicle dynamics and road surface estimation information and utilize the vehicle dynamics information and road surface estimation information to generate a vehicle dynamical context. The system decides which one of a plurality of predefined calibrations is appropriate for the vehicle dynamical context, generates a command to the actuators based on a selected calibration. The system continuously and recursively causes the program code portions to execute while the vehicle is being operated.

Abstract: Systems and methods for determining whether a vehicle is in an understeer or oversteer situation. The system includes a controller circuit coupled to an IMU and an EPS, and programmed to: calculate, for a steered first axle, an axle-based pneumatic trail for using IMU measurements and EPS signals and estimate a saturation level as a function of a distance between the axle-based pneumatic trail and zero. The system estimates, for an unsteered second axle, an axle lateral force curve with respect to a slip angle of the second axle, and a saturation level as a function of when the axle lateral force curve with respect to the slip angle transitions from positive values to negative values. The saturation level of the first axle and the second axle are integrated. The system determines that the vehicle is in an understeer or oversteer situation as a function of the integrated saturation levels.

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 system for managing vehicle body and wheel motion control with a dual clutch differential includes sensors and actuators disposed on the vehicle, the sensors measuring real-time static and dynamic data and the actuators altering static and dynamic behavior of the motor vehicle. A control module executes program code portions stored in memory. The program code portions receive the real-time static and dynamic data; selectively prioritize torque output from a prime mover of the vehicle through the differential to driven wheels of the vehicle to control a body and the driven wheels; model and estimate clutch torque for each clutch of the dual clutch differential; model and estimate a joint clutch torque, a tire force, and corner torque; and generate a torque output for each clutch of the dual clutch differential that is selected to maintain one or more of body control, wheel control, and stability of the motor vehicle.

Abstract: A computing system including a cooperative system architecture for cataloging, informing, sharing, and managing engineering assets within an organization includes a user management subsystem for creating a registered user profile associated with a specific user of the computing system. The registered user profile includes user metadata that provides identifying characteristics of the specific user. The computing system includes an asset management subsystem including an asset repository for recording one or more engineering assets. The asset management subsystem modifies the user metadata of the registered user profile in response to the specific user recording an engineering asset in the asset repository. The computing system includes a test management subsystem including a test repository for recording one or more test bill of materials.

Abstract: A system and method of automatic image view alignment for a camera-based road condition detection on a vehicle. The method includes transforming a fisheye image into a non-distorted subject image, comparing the subject image with a reference image, aligning the subject image with the reference image, and analyzing the aligned subject image to detect and identify road conditions in real-time as the vehicle is in operation. The subject image is aligned with the reference image by determining a distance (d) between predetermined feature points of the subject and reference images, estimating a pitch of a projection center based on the distance d, and generating an aligned subject image by applying a rectification transformation on the fisheye image by relocating a center of projection of the fisheye image by the pitch angle .

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 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 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.