# Anomaly Detection in Autonomous Driving: A Survey

arXiv: https://arxiv.org/abs/2204.07974 · HTML: https://mlsanity.com/paper/2204.07974/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2022.

## Built on by (most cited first, 30 of 87)

- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario](https://mlsanity.com/paper/2305.14836/) (2023)
- [LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion](https://mlsanity.com/paper/2303.03595/) (2023)
- [A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect](https://mlsanity.com/paper/2401.16402/) (2024)
- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection](https://mlsanity.com/paper/2305.09999/) (2023)
- [YOLOE: Real-Time Seeing Anything](https://mlsanity.com/paper/2503.07465/) (2025)
- [VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models](https://mlsanity.com/paper/2412.01095/) (2024)
- [Segment Every Out-of-Distribution Object](https://mlsanity.com/paper/2311.16516/) (2023)
- [Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?](https://mlsanity.com/paper/2308.06619/) (2023)
- [Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.02790/) (2023)
- [Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection](https://mlsanity.com/paper/2301.09489/) (2023)
- [Towards Trustworthy Dataset Distillation](https://mlsanity.com/paper/2307.09165/) (2023)
- [NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation](https://mlsanity.com/paper/2311.11961/) (2023)
- [AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2405.07865/) (2024)
- [OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection](https://mlsanity.com/paper/2503.16247/) (2025)
- [Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection](https://mlsanity.com/paper/2505.15205/) (2025)
- [Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification](https://mlsanity.com/paper/2404.18573/) (2024)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [HiProbe-VAD: Video Anomaly Detection via Hidden States Probing in Tuning-Free Multimodal LLMs](https://mlsanity.com/paper/2507.17394/) (2025)
- [Calibrated Out-of-Distribution Detection with a Generic Representation](https://mlsanity.com/paper/2303.13148/) (2023)
- [Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05701/) (2023)
- [LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis](https://mlsanity.com/paper/2310.00952/) (2023)
- [OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction](https://mlsanity.com/paper/2503.10605/) (2025)
- [ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection](https://mlsanity.com/paper/2210.10495/) (2022)
- [Self-Aware Trajectory Prediction for Safe Autonomous Driving](https://mlsanity.com/paper/2305.09147/) (2023)
- [Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets](https://mlsanity.com/paper/2401.17013/) (2024)
- [Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving](https://mlsanity.com/paper/2504.19183/) (2025)

## Abstract (arXiv)

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.