用对齐世界模型让强化学习直接从原始传感器数据开车，效果领先。
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
- 双流架构：先训带特权信息的世界模型，再用引导机制对齐原始传感器模型。
- 在CARLA v2.0榜单上唯一基于RL的端到端方案，性能达新高。
- 适合研究强化学习与自动驾驶融合的学者和工程师。
强化学习（RL）能缓解模仿学习（IL）固有的因果混淆与分布偏移问题。然而，将RL应用于端到端自动驾驶（E2E-AD）仍面临训练困难，目前工业界和学术界仍以模仿学习为主。近期基于模型的强化学习（MBRL）在神经规划中展现出良好前景，但这些方法通常需要特权信息作为输入，而非原始传感器数据。为此，我们提出Raw2Drive，一种双流式MBRL方法。首先，高效训练一个使用特权信息的辅助世界模型及配套神经规划器；随后，引入通过所提引导机制训练的原始传感器世界模型，确保其在回放过程中的输出与特权世界模型一致；最后，原始传感器世界模型利用特权世界模型头部中嵌入的先验知识，有效指导原始传感器策略的训练。Raw2Drive是目前唯一在CARLA Leaderboard 2.0和Bench2Drive上基于强化学习的端到端自动驾驶方法，达到当前最优性能。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E-AD) remains an open problem for its training difficulty, and IL is still the mainstream paradigm in both academia and industry. Recently Model-based Reinforcement Learning (MBRL) have demonstrated promising results in neural planning; however, these methods typically require privileged information as input rather than raw sensor data. We fill this gap by designing Raw2Drive, a dual-stream MBRL approach. Initially, we efficiently train an auxiliary privileged world model paired with a neural planner that uses privileged information as input. Subsequently, we introduce a raw sensor world model trained via our proposed Guidance Mechanism, which ensures consistency between the raw sensor world model and the privileged world model during rollouts. Finally, the raw sensor world model combines the prior knowledge embedded in the heads of the privileged world model to effectively guide the training of the raw sensor policy. Raw2Drive is so far the only RL based end-to-end method on CARLA Leaderboard 2.0, and Bench2Drive and it achieves state-of-the-art performance.
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