Vehicle Trajectory Planning Using Model Predictive Control in Environments With Dynamic and Static Obstacles

The ability of automated vehicles to avoid obstacles on the road is one of the most critical functionalities, as it directly impacts passenger safety. The trajectory planning functionality plays a key role in this task, as it uses data from sensors to identify obstacles and plan a free obstacle path. Thus, this paper proposes a trajectory planning method based on a model predictive control (MPC) strategy that takes into account both static and dynamic obstacles. Additionally, a trajectory tracking controller based on a linear quadratic regulator (LQR) algorithm is designed to ensure the vehicle follows the planned path. To evaluate the proposed control solution for the lateral dynamics of the vehicle, a traffic scenario involving multiple obstacles (both static and dynamic) is used. The results show that the proposed solution effectively controls the vehicle's lateral dynamics and successfully avoids collisions with obstacles.