Benchmark RadarSENSE-VAD
SENSE-VAD is a synthetic video anomaly detection dataset for autonomous driving, generated with CARLA and Unreal Engine. It includes socially complex anomalies across five categories with per-frame binary labels, plus real-world videos for sim-to-real transfer.
- Released
- 2026-06-30
- Readiness
- Paper only
- Primary field
- Transport & Logistics
Why it matters
Addresses the evaluation gap for socially complex anomalies in autonomous driving, which are not captured by motion-based detectors. Provides a controlled benchmark to test current video anomaly detection models.
Motivation
Autonomous vehicles (AVs) must navigate not only motion-based hazards but also socially complex situations whose danger is constituted by inter-agent relationships rather than movement statistics alone.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.