用自监督嵌入检测自动驾驶数据异常，无需标注也能发现未知问题。
Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings
- 用JEPA构建无标签自监督模型，生成物体状态的深层表征。
- 在nuScenes数据集上实现高精度异常检测，对未知异常敏感。
- 适合真实道路场景中持续监控，应对突发未知故障。
随着自动驾驶车辆投入使用，保障其安全运行至关重要。为监控已部署系统，需采用持续在线的监测框架，实时评估系统状态并记录异常。本文提出一种基于自监督嵌入的在线监测框架，用于检测物体状态表示中的异常。核心挑战在于：未知异常通常缺乏标注数据。为此，本文构建基于JEPA的自监督预测任务，无需异常标签即可训练出丰富的物体嵌入表示。这些表达力强的JEPA嵌入作为输入，接入经典异常检测方法，实现对物体状态异常的识别。该框架特别适用于真实道路环境，在运行中面对未见过的异常时仍具有效性。在公开的真实世界nuScenes数据集上的实验验证了其能力。
原文摘要 · Abstract (English)
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.
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