mlsanityData to 14 Oct 2026

AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving

arXiv 2405.07865 ↗First posted 13 May 2024cs.CV cs.ROcited by 14

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Abstract (arXiv)

The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary to detect anomalies in the first place. Anomaly detection for autonomous driving has made great progress in the past years but suffers from poorly designed benchmarks with a strong focus on camera data. In this work, we propose AnoVox, the largest benchmark for ANOmaly detection in autonomous driving to date. AnoVox incorporates large-scale multimodal sensor data and spatial VOXel ground truth, allowing for the comparison of methods independent of their used sensor. We propose a formal definition of normality and provide a compliant training dataset. AnoVox is the first benchmark to contain both content and temporal anomalies.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 60 of its 82 references that are in the map. First posted on arXiv 13 May 2024.

Builds on

60 of 82 references.

References · 60

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38 more
  1. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 194
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 7.

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  1. 2025Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Drivingcited by 10
  2. 2024OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detectioncited by 9
  3. 2025Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilitiescited by 6
  4. 2024UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Drivingcited by 3
  5. 2025A Data-Driven Novelty Score for Diverse In-Vehicle Data Recordingcited by 3
  6. 2025A Dataset for Semantic Segmentation in the Presence of Unknownscited by 2
  7. 2025Anomalous Decision Discovery using Inverse Reinforcement Learningcited by 0
  8. 2026ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Predictioncited by 0
  9. 2026AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Drivingcited by 0
  10. 2026Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detectioncited by 0
  11. 2026Energy-Aware NECO for Single-Pass Pixel-wise Out-of-Distribution Detection in Semantic Segmentationcited by 0
  12. 2026SENSE-VAD: Sentient and Semantic Video Anomaly Detection for Autonomous Drivingcited by 0
2 more
  1. 2026Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Drivingcited by 0
  2. 2026NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessmentcited by 0