Shih-Ken Chen

Bio: Shih-Ken Chen is an academic researcher from General Motors. The author has contributed to research in topics: Vehicle dynamics & Control theory. The author has an hindex of 27, co-authored 137 publications receiving 2456 citations.

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Abstract: Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities.

An optimal torque distribution control strategy for four-independent wheel drive electric vehicles

Abstract: In this paper, an optimal torque distribution approach is proposed for electric vehicle equipped with four independent wheel motors to improve vehicle handling and stability performance. A novel objective function is formulated which works in a multifunctional way by considering the interference among different performance indices: forces and moment errors at the centre of gravity of the vehicle, actuator control efforts and tyre workload usage. To adapt different driving conditions, a weighting factors tuning scheme is designed to adjust the relative weight of each performance in the objective function. The effectiveness of the proposed optimal torque distribution is evaluated by simulations with CarSim and Matlab/Simulink. The simulation results under different driving scenarios indicate that the proposed control strategy can effectively improve the vehicle handling and stability even in slippery road conditions.

Steering Assist in Driver Initiated Collision Avoidance Maneuver

Abstract: A collision avoidance system for assisting a driver in avoiding a collision between a host vehicle and obstacle. A processor recursively calculates a time-to-collision with the obstacle and an optimal collision avoidance path for avoiding the collision. The optimum collision avoidance path is recursively generated based on a position and speed of the host vehicle relative to the obstacle and an updated calculated time-to-collision. A sensing device determines whether the driver of the vehicle has initiated a steering maneuver to avoid the obstacle. A steering assist mechanism maintains the host vehicle along the optimum collision avoidance path. The steering assist mechanism applies a steering assist torque for producing steering adjustments to assist in guiding the host vehicle along the optimum collision avoidance path to the target lane. The steering assist torque generated by the steering assist mechanism is recursively adjusted based on a recent updated optimum collision avoidance path.

Vehicle collision avoidance and warning system

Abstract: A collision avoidance system in a host vehicle that employs combined automatic braking and steering. The collision avoidance system defines thresholds that identify a time to collision with a target vehicle by the host vehicle that are based on the speed of the host vehicle, the acceleration of the host vehicle, the speed of the target vehicle, the acceleration of the target vehicle, the distance to the target vehicle from the host vehicle and a coefficient of friction of the roadway. The collision avoidance system provides full automatic collision avoidance braking if the time to collision is less than one threshold and the lane adjacent to the host vehicle is not clear. The collision avoidance system provides both automatic steering and braking of the host vehicle if the time to collision is less than another threshold and the lane adjacent to the host vehicle is clear.

Display screen or portion thereof with graphical user interface

Abstract: Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Linear And Nonlinear Programming

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Digital control of dynamic systems

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A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Abstract: Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities.

Vehicle control system, vehicle control method, and vehicle control program

Abstract: A vehicle control device includes a detecting unit that detects a towing state of a subject vehicle, a recognizing unit that recognizes surrounding situations of the subject vehicle, a control unit that performs automatic control in which at least one of acceleration-and-deceleration and steering of the subject vehicle is automatically controlled on the basis of the surrounding situations of the subject vehicle recognized by the recognizing unit, and a changing unit that changes, if the detecting unit has detected that the subject vehicle is in a state of towing an object, details of control performed by the control unit in such a manner that it is less likely to perform the automatic control than in the case where the detecting unit has not detected that the subject vehicle is in a state of towing an object