TY - GEN
T1 - Event-triggered MPC for Collision Avoidance of Autonomous Vehicles Considering Trajectory Tracking Performance
AU - Wang, Jiarun
AU - Guo, Yuanbo
AU - Wang, Quanfeng
AU - Gao, Jian
AU - Chen, Yimin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Collision avoidance strategy is essential for driving safety of autonomous vehicles. Due to the discrepancy between vehicle motion planning and control, it is challenging to plan and track the collision-free trajectories that are adaptive to trajectory tracking errors and feasible to be executed. The paper puts forward a novel collision avoidance method for autonomous vehicles using the event-triggered MPC algorithm. By developing the triggering conditions as the trajectory tracking errors, vehicle motion planning and control are applied at different frequencies, which reduces the computational cost and guarantees the collision avoidance performances. Furthermore, the collision avoidance constraints, together with vehicle kinematic and dynamic models, are included in the proposed method, so that self-driving vehicles can track planned trajectories and the obstacles can be avoided. Simulation results prove the suggested way can generate and track a feasible trajectory to avoid the obstacle and greatly reduce the computational load.
AB - Collision avoidance strategy is essential for driving safety of autonomous vehicles. Due to the discrepancy between vehicle motion planning and control, it is challenging to plan and track the collision-free trajectories that are adaptive to trajectory tracking errors and feasible to be executed. The paper puts forward a novel collision avoidance method for autonomous vehicles using the event-triggered MPC algorithm. By developing the triggering conditions as the trajectory tracking errors, vehicle motion planning and control are applied at different frequencies, which reduces the computational cost and guarantees the collision avoidance performances. Furthermore, the collision avoidance constraints, together with vehicle kinematic and dynamic models, are included in the proposed method, so that self-driving vehicles can track planned trajectories and the obstacles can be avoided. Simulation results prove the suggested way can generate and track a feasible trajectory to avoid the obstacle and greatly reduce the computational load.
KW - Autonomous vehicles
KW - Collision avoidance
KW - Event-triggered MPC
KW - Motion Planning and Control
UR - http://www.scopus.com/inward/record.url?scp=85143739967&partnerID=8YFLogxK
U2 - 10.1109/ICARM54641.2022.9959447
DO - 10.1109/ICARM54641.2022.9959447
M3 - 会议稿件
AN - SCOPUS:85143739967
T3 - ICARM 2022 - 2022 7th IEEE International Conference on Advanced Robotics and Mechatronics
SP - 514
EP - 520
BT - ICARM 2022 - 2022 7th IEEE International Conference on Advanced Robotics and Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2022
Y2 - 9 July 2022 through 11 July 2022
ER -