Integrated trajectory planning and tracking control for FWID-AV collision avoidance with multi-actuator control distribution

As a major development direction of modern automobiles, autonomous vehicles can significantly improve driving safety, efficiency, and economic benefits, making them an urgent societal need. However, existing reports often overlook the impact of prediction inaccuracies in obstacle motion, particularly in complex traffic scenarios. In addition, local trajectory planning and tracking are typically treated as separate tasks, which may lead to poor tracking accuracy of the local reference trajectory and even controller failure. With that in mind, this paper focuses on four-wheel-independent-drive autonomous vehicles (FWID-AV) and proposes an integrated control framework that considers local trajectory planning and trajectory tracking for dynamic obstacle avoidance based on Model Predictive Control (MPC) and Particle Swarm Optimization (PSO) algorithms. Firstly, the vehicle dynamics model is established considering the specific functional requirements of AV trajectory planning and tracking control. Then, a new planning-integrated controller is developed for FWID-AV to achieve collision avoidance. Finally, the MATLAB/Simulink-CarSim joint simulation platform is built, and the effectiveness of the proposed integrated control framework is verified through simulation and experimental tests, which enhances the performance of collision avoidance, trajectory tracking, handling stability, and real-time performance for vehicle.