Research of Trajectory Planning and Position Attitude Tracking for Vehicle Obstacle Avoidance Utilizing the Model Predictive Control (MPC) Algorithm

In the rapidly evolving domain of intelligent vehicle systems, ensuring safety and efficiency in navigation through dynamic environments poses significant challenges. Our study introduces a comprehensive framework that integrates both linear time-varying and nonlinear MPC strategies to enhance the active safety systems of intelligent vehicles. By adopting a multifaceted approach that encompasses the Point Mass Model for high-speed scenarios and the Two-Degree-of-Freedom Model for intricate maneuvering, we offer a versatile solution to trajectory planning. The core of our analysis focuses on the formulation of a control strategy that adeptly manages lateral dynamics and positional deviations, ensuring optimal path tracking and vehicle stability. We further enrich our model by incorporating a novel repulsive potential field method tailored to predict and navigate around obstacles dynamically, thereby mitigating collision risks. Extensive simulation experiments underscore the efficacy of our proposed framework, demonstrating significant improvements in driving stability and comfort.