Citation

Wang H, Huang Y, Khajepour A, Zhang Y, Rasekhipour Y, Cao D. IEEE Trans. Intel. Transp. Syst. 2019; 20(9): 3313-3323.

Copyright

(Copyright © 2019, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2018.2873921

PMID

unavailable

Abstract

A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.

Language: en

Keywords

Accidents; artificial potential field; Automobiles; autonomous vehicles; Autonomous vehicles; collision avoidance; controller objective; crash mitigation; Crash mitigation; crash severity; Heuristic algorithms; mobile robots; model predictive control algorithm; model predictive controller; model predictive motion planning system; motion planning; motion planning method; MPC; MPC algorithm; obstacle avoidance; optimal control; optimal control problem; path planning; Planning; potential crash severity; predictive control; remotely operated vehicles; road safety; road traffic control; Roads; safety; vehicle dynamic; vehicle dynamics; Vehicle dynamics