Flexible Trajectory Planning for Autonomous Vehicles Via Environmental Assessment in Extreme Scenarios

Abstract

Trajectory planning is a core task in autonomous driving. However, in diverse extreme scenarios characterized by unstructured obstacles, there is a lack of solutions that provide efficient computation, safety, and scene generalization capabilities. To address this issue, we propose a two-stage spatio-temporal joint trajectory planning method based on environmental assessment. In the first stage, we introduce the EAHybrid A* algorithm, which generates high-quality initial trajectories by evaluating environmental complexity, thereby significantly improving computational efficiency. The second stage formulates the trajectory planning problem as an optimal control problem, utilizing environmental assessment for joint spatio-temporal optimization, ensuring kinematic feasibility and obstacle avoidance. Experiments demonstrate that our method achieves higher success rates and planning speeds in extreme scenarios compared to state-of-the-art planning methods. Moreover, we have deployed and validated this approach in the CARLA simulator and real vehicles, proving its effectiveness and robustness in handling extreme environments.