Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection

ArXi:2605.19744v1 Announce Type: new Detecting anomalies in traffic scenes is crucial for ensuring safety in autonomous driving, yet collecting representative anomalous data remains challenging. Existing anomaly detection methods are highly specialized and rely on normality as defined by the abstract semantic Cityscapes classes, making it difficult to adapt to diverse real-world scenarios.