As autonomous vehicles are rolled out, measures must be taken to ensure their safe operation. In order to supervise a system that is already in operation, monitoring frameworks are frequently employed. These run continuously online in the background, supervising the system status and recording anomalies. This work proposes an online monitoring framework to detect anomalies in object state representations. Thereby, a key challenge is creating a framework for anomaly detection without anomaly labels, which are usually unavailable for unknown anomalies. To address this issue, this work applies a self-supervised embedding method to translate object data into a latent representation space. For this, a JEPA-based self-supervised prediction task is constructed, allowing training without anomaly labels and the creation of rich object embeddings. The resulting expressive JEPA embeddings serve as input for established anomaly detection methods, in order to identify anomalies within object state representations. This framework is particularly useful for applications in real-world environments, where new or unknown anomalies may occur during operation for which there are no labels available. Experiments performed on the publicly available, real-world nuScenes dataset illustrate the framework's capabilities.

翻译：随着自动驾驶车辆的部署，必须采取措施确保其安全运行。为监督已投入运行的系统，监控框架常被采用。这些框架在后台持续在线运行，监控系统状态并记录异常。本研究提出一种在线监控框架，用于检测对象状态表征中的异常。其中，关键挑战在于构建无需异常标签的异常检测框架——对于未知异常，此类标签通常无法获取。为解决该问题，本研究采用自监督嵌入方法将对象数据转换至潜在表征空间。为此，构建了基于JEPA的自监督预测任务，使得无需异常标签即可训练，并生成丰富的对象嵌入。所得具有强表征能力的JEPA嵌入可作为成熟异常检测方法的输入，以识别对象状态表征中的异常。该框架对于现实环境中的应用尤为有效，因为在运行过程中可能出现新的或未知的异常，且无法获得相应标签。在公开的真实世界nuScenes数据集上进行的实验验证了该框架的有效性。