Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings
This addresses the challenge of monitoring autonomous vehicles for unknown anomalies in real-world environments, but it is incremental as it applies existing methods in a new framework.
This work tackled the problem of detecting anomalies in object state representations for autonomous vehicles without requiring anomaly labels, by proposing an online monitoring framework that uses JEPA-based self-supervised embeddings as input to established anomaly detection methods, with experiments on the nuScenes dataset demonstrating its capabilities.
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.