Secondary crash mitigation controller after rear-end collisions using reinforcement learning.

[Display omitted] • A controller to prevent secondary crashes after an initial rear-end collision is proposed. • Rule-based switching control is embedded into reinforcement learning. • Pre-collision control and post-collision control are combined. • Results prove the superiority of the proposed controller. Rear-end collisions result in a large number of casualties and property losses, and the serious injury risk in multiple impact accidents is much higher than that in single impact accidents. In this paper, we propose a novel controller to facilitate the prevention of secondary crashes after an initial rear-end collision, which expands the operational horizon of conventional vehicle active safety systems from preventive measures to post-event mitigation measures. Considering the complexity of the problem with multi-object synthesis optimization and vehicle nonlinear dynamics, this study combines the pre-collision control and post-collision control to reduce the initial crash loss and the subsequent control difficulty. The rule-based switching control and drift manipulation are embedded into the reinforcement learning algorithm to improve the training efficiency and control performance. The bench test results validate the superiority of the proposed controller over other strategies and algorithms in different rear-end collision scenarios. [ABSTRACT FROM AUTHOR]