Abstract

Autonomous driving systems not only provide services for human drivers, but also need to consider the personalized driving requirements of human beings. First, a Proportional Integral Differential (PID) feedback channel is introduced in a traditional Model Predictive Control (MPC) to improve the performance of the controller. Then based on the Chance Constrained Programming, the control constraint is combined with driver personalization to reflect a variety of driving personality characteristics. Finally, the proposed method is tested using Hardware-in-the-loop (HIL) experiments. The experiment results demonstrate that the proposed method can successfully implement vehicle tracking control and make the vehicle’s state of movement match the driver’s expectations, which can increase driver comfort and driving safety.

Keywords:Autonomous vehicle; MPC; Driving styles; Vehicle control; Personalized constraints