Multi-Objective Trajectory Optimization Method for Connected Autonomous Vehicles Based on Risk Potential Field

The planning of trajectories for Connected Autonomous Vehicles (CAVs) represents a pivotal aspect of autonomous driving technologies, enabling secure navigation within traffic environments. Traditional models for trajectory control primarily focus on the efficiency and safety of individual vehicles but often overlook the dynamics involved in vehicle-to-vehicle and vehicle-to-infrastructure interactions. This study introduces a novel concept, the “driving risk field,” which imposes constraints on vehicular movement within designated road spaces to enhance safety. A vehicle dynamics model is developed, employing a non-linear fifth-degree polynomial to approximate the trajectory curves, with optimization performed using the Sequential Quadratic Programming (SQP) method. The efficacy of the optimized model is validated through simulations on the Prescan/Simulink platform, demonstrating a 17.9% reduction in trajectory angle slopes and a 23.4% decrease in lateral and longitudinal errors compared to conventional Model Predictive Control (MPC), Pure-Pursuit (PP) and Linear Quadratic Regulator (LQR) models. This approach significantly enhances vehicle control in traffic bottleneck areas, indicating superior trajectory adaptation.