A humanoid collision-avoidance strategy considering a large sideslip angle state

In high-speed collision-avoidance scenarios, collision-avoidance requirements in some extreme situations conflict with vehicle stability requirements. Professional drivers deal with these situations in competitions by drifting. Inspired by this technique, this paper proposed a humanoid collision-avoidance strategy that allows vehicles to perform emergency collision-avoidance tasks with a temporary large sideslip angle state. The proposed strategy imitated the reaction process of human drivers and divided the collision-avoidance process into three stages. The first stage was the collision-avoidance stage, in which the target turned the travel direction rapidly by generating a large heading angular velocity. The second stage was the transitional stage, in which the vehicle states were mitigated on the premise that the heading angle was not reduced until the obstacle and the vehicle did not overlap. In the third stage, the vehicle was guided into the side lane by model predictive control. Throughout this process, the vehicle was allowed to temporarily break the stability constraints. Additionally, this paper proposed a regression region constraint to ensure that the vehicle could return to its stability region. Simulations in a variety of conditions and model-in-loop (MIL) experiments were conducted. According to the simulation results, the proposed control strategy could drive the vehicle out of the stability region to deal with more extreme conditions under a regression region constraint. The collision can be avoided in shorter longitudinal and lateral distances by the proposed control strategy. Besides, the MIL experiment verified the real-time performance of the proposed system.