In the domain of autonomous driving, the Learning from Demonstration (LfD) paradigm has exhibited notable efficacy in addressing sequential decision-making problems. However, consistently achieving safety in varying traffic contexts, especially in safety-critical scenarios, poses a significant challenge due to the long-tailed and unforeseen scenarios absent from offline datasets. In this paper, we introduce the saFety-aware strUctured Scenario representatION (FUSION), a pioneering methodology conceived to facilitate the learning of an adaptive end-to-end driving policy by leveraging structured scenario information. FUSION capitalizes on the causal relationships between decomposed reward, cost, state, and action space, constructing a framework for structured sequential reasoning under dynamic traffic environments. We conduct rigorous evaluations in two typical real-world settings of distribution shift in autonomous vehicles, demonstrating the good balance between safety cost and utility reward of FUSION compared to contemporary state-of-the-art safety-aware LfD baselines. Empirical evidence under diverse driving scenarios attests that FUSION significantly enhances the safety and generalizability of autonomous driving agents, even in the face of challenging and unseen environments. Furthermore, our ablation studies reveal noticeable improvements in the integration of causal representation into the safe offline RL problem.

自动驾驶中，借助结构化场景信息的安全感知结构化场景表示（FUSION）方法的引入为学习自适应的端到端驾驶策略提供了创新方法。通过构建基于因果关系的奖励、成本、状态和行动空间分解的框架，FUSION 在动态交通环境下进行结构化顺序推理，实现了安全成本和效用回报之间的良好平衡，提高了自动驾驶代理在多样的驾驶场景中的安全性和普适性。

安全感知的因果表示法：自主驾驶中可信赖强化学习