用扩散模型实现自动驾驶安全自适应路径规划，成功率96%零碰撞。
IRL-DAL: Safe and Adaptive Trajectory Planning for Autonomous Driving via Energy-Guided Diffusion Models
- 基于扩散模型的动态前瞻规划，结合逆强化学习与环境反馈。
- 训练后成功率达96%，每千步碰撞仅0.05次，性能达新基准。
- 适合追求高安全性的自动驾驶系统研发人员参考。
本文提出一种基于扩散模型的逆强化学习框架（IRL-DAL），用于自动驾驶车辆的安全自适应路径规划。训练初期通过模仿专家有限状态机（FSM）控制器实现稳定初始化，将环境项与逆强化学习判别器信号结合，对齐专家目标。随后采用混合奖励进行强化学习，融合扩散环境反馈与定向逆强化学习奖励。一个条件扩散模型作为安全监督器，确保路径保持车道、避障并平滑行驶。可学习自适应掩码（LAM）根据车速和周边危险调整视觉注意力。在Webots仿真中采用两阶段课程训练，最终实现96%的成功率，碰撞率降至每1000步0.05次，达到安全导航新基准。该方法使智能体不仅可保持车道，还能以专家级水平应对危险场景，显著提升鲁棒性。代码已公开。
原文摘要 · Abstract (English)
This paper proposes a novel inverse reinforcement learning framework using a diffusion-based adaptive lookahead planner (IRL-DAL) for autonomous vehicles. Training begins with imitation from an expert finite state machine (FSM) controller to provide a stable initialization. Environment terms are combined with an IRL discriminator signal to align with expert goals. Reinforcement learning (RL) is then performed with a hybrid reward that combines diffuse environmental feedback and targeted IRL rewards. A conditional diffusion model, which acts as a safety supervisor, plans safe paths. It stays in its lane, avoids obstacles, and moves smoothly. Then, a learnable adaptive mask (LAM) improves perception. It shifts visual attention based on vehicle speed and nearby hazards. After FSM-based imitation, the policy is fine-tuned with Proximal Policy Optimization (PPO). Training is run in the Webots simulator with a two-stage curriculum. A 96\% success rate is reached, and collisions are reduced to 0.05 per 1k steps, marking a new benchmark for safe navigation. By applying the proposed approach, the agent not only drives in lane but also handles unsafe conditions at an expert level, increasing robustness.We make our code publicly available.
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