mlsanityData to 7 Oct 2026

Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving

arXiv 2406.06423 ↗First posted 10 Jun 2024cs.CV cs.ROcited by 1

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Abstract (arXiv)

In autonomous driving, the most challenging scenarios can only be detected within their temporal context. Most video anomaly detection approaches focus either on surveillance or traffic accidents, which are only a subfield of autonomous driving. We present HF2-VADAD, a variation of the HF2-VAD surveillance video anomaly detection method for autonomous driving. We learn a representation of normality from a vehicle's ego perspective and evaluate pixel-wise anomaly detections in rare and critical scenarios.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 62 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

29 of 62 references.

References · 29

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 780
  3. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  4. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  5. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  6. 2016Learning Temporal Regularity in Video Sequencescited by 254
  7. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  8. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  9. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  10. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  11. 2022Anomaly Detection in Autonomous Driving: A Surveycited by 87
  12. 2023DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Drivingcited by 73
17 more
  1. 2020Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Eventscited by 70
  2. 2019Unsupervised Traffic Accident Detection in First-Person Videoscited by 69
  3. 2019Misbehaviour Prediction for Autonomous Driving Systemscited by 55
  4. 2024Abductive Ego-View Accident Video Understanding for Safe Driving Perceptioncited by 43
  5. 2019Towards Corner Case Detection for Autonomous Drivingcited by 39
  6. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  7. 2021Description of Corner Cases in Automated Driving: Goals and Challengescited by 35
  8. 2021An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Drivingcited by 33
  9. 2023Vision-Based Traffic Accident Detection and Anticipation: A Surveycited by 31
  10. 2019Anomaly Detection in Road Traffic Using Visual Surveillance: A Surveycited by 30
  11. 2021DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behaviorcited by 17
  12. 2020Towards Anomaly Detection in Dashcam Videoscited by 16
  13. 2021Anomaly Detection in Multi-Agent Trajectories for Automated Drivingcited by 15
  14. 2024Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantificationcited by 11
  15. 2024Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videoscited by 9
  16. 2022Performance Analysis of Out-of-Distribution Detection on Trained Neural Networkscited by 7
  17. 2023Learning Representation for Anomaly Detection of Vehicle Trajectoriescited by 6

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  1. 2025Towards Vision Zero: The TUM Traffic Accid3nD Datasetcited by 2