Visual anomaly detection can make autonomous vehicles safer by identifying unfamiliar objects and alerting drivers to anomalies, with lightweight models proving practical for real-world deployment on edge devices.
This paper benchmarks visual anomaly detection methods for autonomous driving, testing eight state-of-the-art models on a large synthetic dataset to identify unfamiliar objects and hazards.