Integrated Post-Impact Planning and Active Safety Control for Autonomous Vehicles

How to reduce traffic accidents and improve vehicle safety has always aroused widespread concern. Secondary and serial collision accidents can result in more serious hazards as the initial collision can easily destabilize a high-speed vehicle and the driver may fail to maintain effective control due to panic. With this in mind, a planning-integrated active safety control system is developed for post-impact vehicles to avoid subsequent accidents. The vehicle dynamics model is established considering the roll degree of freedom. The constraint equivalent methods based on the octagon and rhombus envelopes are proposed to linearize the road adhesion constraint and the obstacle avoidance constraint, respectively. A planning-integrated model predictive controller (MPC) is developed for post-impact vehicles to achieve the coordination of stability recovery and the avoidance of secondary collision with surrounding vehicles. In the meantime, an obstacle avoidance decision strategy based on the safe braking distance is designed to cope with different traffic scenes. Furthermore, a control allocator based on particle swarm optimization is developed to achieve the high-efficiency allocation of the resultant control output of MPC under post-impact extreme conditions. The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.

Automated summary

Reducing traffic accidents and improving vehicle safety have always been a matter of great concern. To avoid secondary and serial collision accidents, a planning-integrated active safety control system is developed for post-impact vehicles. This system considers the roll degree of freedom and establishes a vehicle dynamics model. It proposes constraint equivalent methods based on octagon and rhombus envelopes to linearize the road adhesion constraint and obstacle avoidance constraint, respectively. Moreover, it includes an obstacle avoidance decision strategy based on safe braking distance and a control allocator based on particle swarm optimization for efficient control allocation under extreme conditions. The proposed scheme is verified through hardware-in-loop tests under comprehensive driving scenarios.