Trajectory Planning Based on Model Predictive Control with Dynamic Obstacle Avoidance in Unstructured Environments

Trajectory planning at low speed applications can involve a large variety of different scenarios, including structured and unstructured environments, pedestrians, cyclists, etc. In this context, real-time planning is crucial to the dexterity of autonomous vehicles. In this paper, a real-time trajectory planner based on model predictive control (MPC) is proposed. Moreover, a dynamic obstacle avoidance and narrow passages control are designed in a scalable way through continuous updates of the optimization constraints. The performance of the proposed methodology are evaluated with a V-cycle model-based approach, through both model-in-the-loop (MIL) simulations, and real prototype in-vehicle experiments.