Gm Global Technolgoy Operations, Inc.

The University of Waterloo

Yubiao Zhang

Bakhtiar Brian Litkouhi

Shuqing Zeng

SeyedAlireza Kasaiezadeh Mahabadi

Qingrong Zhao

Upali Priyantha Mudalige

Bo Yu

Hualin Tan

Amir Khajepour

Seyedeh Asal Nahidi

James Heath Holbrook

Ehsan Asadi

Seyedalireza Kasaiezadeh Mahabadi

Jin-Jae Chen

Reza Hajiloo

Md Mhafuzul Islam

Shamim Mashrouteh

Daniel Aguilar Marsillach

Jarred James Versical

Ruixing Long

Sahm E Litkouhi

Ali Reza Armiyoon

Edward Joseph Ecclestone

Chao Yu

A system for imitating target vehicle behavior in automated driving includes sensors capturing ego and target vehicle condition information, actuators selectively altering an ego vehicle state, and control modules. The control modules execute a target vehicle imitating (TVI) application. A first TVI control logic estimates a target vehicle state and trajectory. The ego and target vehicle condition information partially define the target vehicle state and trajectory. A second control logic evaluates target vehicle safety and performance constraints. A third control logic selectively initiates an imitation mode of the ego vehicle based on target and ego vehicle statuses relative to the target vehicle safety and performance constraints. A fourth control logic, models the target vehicle and optimizes a planned ego vehicle path subject to actuator constraints. A fifth control logic generates outputs to the actuators to cause the ego vehicle to follow the planned path and imitate the target vehicle.

System and method of imitating target vehicle behavior for automated driving

A vehicle system for determining a road wheel angle of a vehicle includes sensors configured to detect vehicle parameters including at least a steering rack load on a steering rack of the vehicle, and a control module in communication with the sensors. The control module is configured to calculate a kingpin torque based on the sensed steering rack load and a wheel-to-body displacement of the vehicle, determine a roll steer angle based on the wheel-to-body displacement of the vehicle, determine a road wheel angle of the vehicle based on the roll steer angle and the kingpin torque, and generate a signal indicative of the road wheel angle to control at least one vehicle control system. Other example vehicle systems and methods for determining a road wheel angle of a vehicle are also disclosed.

Vehicle systems and methods for estimating road wheel angles

A method for estimating lateral force includes receiving vehicle data. The vehicle includes a plurality of tires. The method further includes using a bicycle model to determine first lateral forces at each of the plurality of tires of the vehicle, using a double-track model to determine second lateral forces at each of the plurality of tires of the vehicle, fusing the first lateral forces determined using the bicycle model and the second lateral forces using the double-track model to determine third lateral forces at each of the plurality of tires of the vehicle, and controlling an actuator of the vehicle using the third lateral forces at each of the plurality of tires of the vehicle.

Integrated tire longitudinal and lateral force estimation methodology

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.

Rule-based adaptive cruise control system for a vehicle

A system for learning-model predictive control (LMPC) with multi-step prediction for motion control of a vehicle includes sensors and actuators. One or more control modules each having a processor, a memory, and input/output (I/O) ports are in communication with the sensors and actuators, the processor executing program code portions stored in the memory. The program code portions cause the sensors and actuators 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. A program code portion estimates actions of the actuators based on the vehicle state information and the driver input, and utilizes the vehicle state information, the driver input, and the estimated actions of the actuators to select one or more models of a physics-based vehicle model and a machine-learning model of the vehicle to selectively adjust commands to the actuators.

Learning-model predictive control with multi-step prediction for vehicle motion control

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.

Trajectory planning system for ensuring maneuvers to avoid moving obstacles exist for an autonomous vehicle

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.

Method of anticipatory control for automated driving

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.

Unified self-supervisory learnable vehicle motion control policy

A method of estimating a performance characteristic of a wheel of a vehicle, includes selecting relevant input features based on wheel dynamics and tire behavior, and collecting experimental data for each of the relevant input features at each of a plurality of vehicle operating conditions. The method further includes manually identifying and labeling wheel stability status over time from the experimental data and calculating tractive limit over time from the experimental data. The method also includes training a tractive limit model and training a wheel stability status model. The method further includes receiving a plurality of testing inputs, wherein each of the plurality of testing inputs is received from a sensor on-board the vehicle or from a controller on-board the vehicle and, processing the received testing inputs in a predetermined machine learning process to calculate in one or more data processors a prediction of the performance characteristic.

Machine learning-based tractive limit and wheel stability status estimation

Machine learning-based predictive dynamics for vehicle motion control

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.

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Sterling Heights, MI, United States of America

Yubiao Zhang