Integrated Trajectory Planning and Tracking Control for Autonomous Vehicles Based on Pseudo-time-to-Collision Risk Assessment Model

In order to improve the obstacle avoidance ability of autonomous vehicles in complex traffic environments, speed planning, path planning, and tracking control are integrated into one optimization problem. An integrated vehicle trajectory planning and tracking control method combining a pseudo-time-to-collision (PTC) risk assessment model and model predictive control (MPC) is proposed. First, a risk assessment model with PTC probability is proposed by considering the differentiation of the risk on the relative motion states of the self and front vehicles, and the obstacle vehicles in the lateral and longitudinal directions. Then, a three-degrees-of-freedom vehicle dynamics model is established, and the MPC cost function and constraints are constructed from the perspective of the road environment as well as the stability and comfort of the ego-vehicle, combined with the PTC risk assessment model to optimize the control. Finally, a complex multi-vehicle obstacle avoidance scenario is built to analyze the PTC risk field. Then, three typical obstacle avoidance scenarios are built and analyzed in comparison with a layered control approach. The results show that the method is able to plan a more accurate and stable driving route than layered control, which guarantees the safety and comfort of the vehicle. The proposed PTC risk assessment model is applicable to the vehicle trajectory planning problem with accurate risk assessment in complex road environments, which improves the safety and adaptability of autonomous vehicles in complex road environments.