RoaD: 通过回滚作为演示实现自动驾驶策略的闭环监督微调
RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policies
AI总结：
RoaD通过利用自动驾驶策略自身的闭环回滚作为额外训练数据，有效缓解协变量偏移问题，提升闭环监督微调性能。
摘要：
自动驾驶策略通常通过开放循环行为克隆来训练人类演示。然而，当在闭环中部署时，这些策略会遭受协变量偏移，导致误差累积。我们引入回滚作为演示（RoaD），一种简单且高效的缓解协变量偏移的方法，通过利用策略自身的闭环回滚作为额外训练数据。在回滚生成过程中，RoaD结合专家指导，将轨迹偏向高质量行为，产生信息丰富且真实的演示用于微调。这种方法能够以比强化学习少多个数量级的数据实现稳健的闭环适应，并避免了先前闭环监督微调（CL-SFT）方法的限制性假设，允许更广泛的应用领域，包括端到端驾驶。我们在WOSAC大型交通模拟基准上展示了RoaD的有效性，其表现与先前的CL-SFT方法相似或更好；并在AlpaSim，一个基于神经重建的高保真端到端驾驶模拟器中，其驾驶得分提高了41%，碰撞减少了54%。
英文摘要：
Autonomous driving policies are typically trained via open-loop behavior cloning of human demonstrations. However, such policies suffer from covariate shift when deployed in closed loop, leading to compounding errors. We introduce Rollouts as Demonstrations (RoaD), a simple and efficient method to mitigate covariate shift by leveraging the policy's own closed-loop rollouts as additional training data. During rollout generation, RoaD incorporates expert guidance to bias trajectories toward high-quality behavior, producing informative yet realistic demonstrations for fine-tuning. This approach enables robust closed-loop adaptation with orders of magnitude less data than reinforcement learning, and avoids restrictive assumptions of prior closed-loop supervised fine-tuning (CL-SFT) methods, allowing broader applications domains including end-to-end driving. We demonstrate the effectiveness of RoaD on WOSAC, a large-scale traffic simulation benchmark, where it performs similar or better than the prior CL-SFT method; and in AlpaSim, a high-fidelity neural reconstruction-based simulator for end-to-end driving, where it improves driving score by 41\% and reduces collisions by 54\%.
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