An aggressive cornering framework for autonomous vehicles combining trajectory planning and drift control

Vehicle slipping may cause an accident while driving. However, professional drivers usually perform high side-slip angle maneuvers, such as drifting to minimize lap time or avoid obstacles. Tracking the desired trajectory while maintaining drift is a challenging task due to the complexity of the vehicle model. In this paper, we first solve a series of minimum-time cornering problems under different initial conditions. The results show that an aggressive cornering can be divided into three segments, including the entry corner stage, the drifting stage, and the exiting stage. We then propose a complete trajectory planning and motion control framework to conduct the drift cornering maneuver. The trajectory planner calculates the speed profile and then updates the initial path by optimizing curvature. A switch-mode control system is proposed for the above three stages to track the reference trajectory, which is based on pure pursuit control and Model Predictive Control (MPC). Finally, we validate the cornering framework by simulation on the Simulink-Carsim software and experiments on a 1/10 scale RC vehicle.