Hierarchical Vehicle Active Collision Avoidance Based on Potential Field Method Chongqing Vehicle Inspection and Research Institute Company, Limited

Format:

Book

Conference/Event

Author/Creator:

Zeng, Jie, author.

Contributor:

Ren, Le

Zheng, Ling

Conference Name:

SAE 2021 Intelligent and Connected Vehicles Symposium Part I (2021-11-04 : Chongqing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

In this paper, a closed loop path planning and tracking control approach of collision avoidance for autonomous vehicle is proposed. The two-level model predictive control (MPC) is proposed for the path planning and tracking. The upper-level MPC is designed based on the simple vehicle kinematic model to calculate the collision-free trajectory and the potential field method is adopted to evaluate the collision risk and generate the cost function of the optimization problem. The lower-level MPC is the trajectory-tracking controller based on the vehicle dynamics model that calculates the desired control inputs. Finally the control inputs are distributed to steering wheel angle and motor torque via optimal control vectoring algorithm. Test cases are established on the Simulink/CarSim platform to evaluate the performance of the controller. The simulation results demonstrate that the proposed control has satisfactory path tracking performance and could avoid the potential collisions effectively during the high way driving

Notes:

Vendor supplied data

Publisher Number:

2021-01-7038

Access Restriction:

Restricted for use by site license