Proactive Risk-Aware Trajectory Planning for Autonomous Driving in Unstructured Environments Via Reinforcement Learning with Adaptive Reward Design

Abstract

Trajectory planning for autonomous driving in dynamic unstructured traffic remains a fundamental challenge. Existing methods are often reactive, i.e., they only respond to observed situations without explicitly anticipating future risks. Moreover, most reinforcement learning based approaches rely on manually crafted reward functions, which limits their adaptability and generalization across complex driving scenarios. In this paper, we propose a novel RL-based trajectory planning framework that integrates proactive obstacle avoidance and adaptive reward learning. Specifically, our planner predicts the future trajectories of surrounding traffic participants as well as potential ghost-probe risk zones, and proactively avoids these high-risk regions during planning. In addition, we introduce a large-model agent that dynamically adjusts the reward signals according to evolving traffic contexts, enabling more adaptive and robust policy learning compared with fixed reward designs. To evaluate our method, we build a high-fidelity simulation environment based on the Peking University campus, which provides realistic unstructured traffic scenarios. Extensive experiments demonstrate that our method significantly improves safety, efficiency, and generalization over state-of-the-art baselines, particularly in scenarios with occlusions and unpredictable behaviors. We may open-source our code and simulation environment for community benefit soon.