HP3: Hierarchical Prediction-Pretrained Planning for Unprotected Left Turn

Abstract

Trajectory planning for unprotected left turns poses a significant challenge in autonomous driving. Reinforcement learning (RL) offers potential, but existing methods often rely on scenario-specific state representations, limiting their adaptability. This paper introduces Hierarchical Prediction-Pretrained Planning (HP3), a generalizable hierarchical RL framework designed for unprotected left turns. HP3 leverages historical trajectories of all vehicles and complete map information to achieve versatile state representation and generalizable scene understanding. Its two-layer architecture predicts semantic behavior (upper layer) and generates corresponding trajectories (lower layer). A scene encoder comprehends trajectories and roads, while a trajectory decoder outputs sequential points. To accelerate convergence, we pretrain the main network on a modified trajectory prediction dataset. Evaluation on a CARLA-based map with complex, unprotected left-turn intersections demonstrates HP3’s superiority over rule-based and simple RL-based methods, highlighting the effectiveness of our pretraining approach for this critical autonomous driving task.