In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter difficulties when confronted with unexpected situations. At the same time, world models emerged in the field of model-based reinforcement learning as a way to enable agents to predict the future depending on potential actions. This led to outstanding results in sparse reward and complex control tasks. This work provides an overview of how world models can be leveraged to perform anomaly detection in the domain of autonomous driving. We provide a characterization of world models and relate individual components to previous works in anomaly detection to facilitate further research in the field.

翻译：近年来，自动驾驶领域取得了显著进展。尽管自动驾驶车辆在封闭场景条件下表现出高性能，但面对意外情况时仍面临挑战。同时，世界模型作为基于模型的强化学习领域的一种方法出现，使智能体能够根据潜在行动预测未来。这在稀疏奖励和复杂控制任务中取得了卓越成果。本文概述了如何利用世界模型在自动驾驶领域执行异常检测。我们提供了世界模型的特征描述，并将各个组件与先前异常检测研究的相关工作进行关联，以促进该领域的进一步研究。