Trajectory Planning and Tracking Control of Autonomous Vehicles based on Improved Artificial Potential Field

At present, trajectory planning and tracking control methods for autonomous vehicles (AVs) are complicated and mainly suitable for specific scenarios. In this paper, based on vehicle dynamics model, a novel approach of cleverly introducing the velocity-related safe distance into the artificial potential field function is proposed. The improved traffic environment potential field is then combined with model predictive control (MPC), and lateral stability constraints such as lateral load transfer rate are added to the MPC when solving for the control quantity. We name this method AutoPField-MPC, and it offers the following advantages: First, it allows AVs to automatically adjust the distance from other obstacles in more complex scenarios, while also dynamically influencing steering and braking time. Second, it reduces extensive parameter adjustment work when considering the safe distance. Third, the improved design of the potential field function in this paper makes the method more concise and universal. The simulation results from CarSim and Simulink demonstrate that the method presented in this paper can simultaneously achieve multiple active safety objectives, such as speed maintenance, self-adaptive distance adjustment, lane keeping, lateral stability, emergency braking, and emergency steering, with good real-time performance. As a result, it can effectively address the security issues of vehicles and pedestrians when AVs avoid obstacles in various complex scenarios, such as wet roads, multi-obstacle vehicles, and emergency obstacle avoidance.