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，自动驾驶技术取得了显著进展。尽管自动驾驶车辆在封闭场景下表现出色，但在面对突发情况时仍面临挑战。与此同时，基于模型的强化学习领域涌现了世界模型，该方法使智能体能够根据潜在动作预测未来状态，并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进行异常检测。我们首先对世界模型进行了特征描述，并将其各组成部分与既有异常检测研究成果相关联，以推动该领域的后续研究。