# Driver Anomaly Detection: A Dataset and Contrastive Learning Approach

arXiv: https://arxiv.org/abs/2009.14660 · HTML: https://mlsanity.com/paper/2009.14660/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 30 of 41)

- [Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection](https://mlsanity.com/paper/2303.13051/) (2023)
- [AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception](https://mlsanity.com/paper/2307.13933/) (2023)
- [Towards Open Set Video Anomaly Detection](https://mlsanity.com/paper/2208.11113/) (2022)
- [DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network](https://mlsanity.com/paper/2211.00882/) (2022)
- [Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions](https://mlsanity.com/paper/2207.08975/) (2022)
- [Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos](https://mlsanity.com/paper/2306.10159/) (2023)
- [A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding](https://mlsanity.com/paper/2204.04734/) (2022)
- [Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2301.00114/) (2022)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss](https://mlsanity.com/paper/2504.17813/) (2025)
- [Contrastive Predictive Coding for Anomaly Detection](https://mlsanity.com/paper/2107.07820/) (2021)
- [TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera](https://mlsanity.com/paper/2108.12196/) (2021)
- [Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos](https://mlsanity.com/paper/2109.04021/) (2021)
- [Visualizing and Understanding Contrastive Learning](https://mlsanity.com/paper/2206.09753/) (2022)
- [GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response](https://mlsanity.com/paper/2210.06186/) (2022)
- [Unsupervised Anomaly Detection from Time-of-Flight Depth Images](https://mlsanity.com/paper/2203.01052/) (2022)
- [Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder](https://mlsanity.com/paper/2211.06870/) (2022)
- [Learning Representation for Anomaly Detection of Vehicle Trajectories](https://mlsanity.com/paper/2303.05000/) (2023)
- [Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition](https://mlsanity.com/paper/2102.12570/) (2021)
- [Real-Time Driver Monitoring Systems through Modality and View Analysis](https://mlsanity.com/paper/2210.09441/) (2022)
- [Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning](https://mlsanity.com/paper/2112.02922/) (2021)
- [A Survey on Datasets for Decision-making of Autonomous Vehicle](https://mlsanity.com/paper/2306.16784/) (2023)
- [Automated Data Curation Using GPS & NLP to Generate Instruction-Action Pairs for Autonomous Vehicle Vision-Language Navigation Datasets](https://mlsanity.com/paper/2505.03174/) (2025)
- [Sage Deer: A Super-Aligned Driving Generalist Is Your Copilot](https://mlsanity.com/paper/2505.10257/) (2025)
- [BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving](https://mlsanity.com/paper/2604.07263/) (2026)
- [Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders](https://mlsanity.com/paper/2209.05269/) (2022)
- [Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention](https://mlsanity.com/paper/2304.06370/) (2023)
- [On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars](https://mlsanity.com/paper/2307.16543/) (2023)
- [Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives](https://mlsanity.com/paper/2403.02772/) (2024)
- [Transformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition](https://mlsanity.com/paper/2403.06577/) (2024)

## Abstract (arXiv)

Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from normal driving) contains the utmost importance to reduce driver-related accidents. However, there are unbounded many anomalous actions that a driver can do while driving, which leads to an 'open set recognition' problem. Accordingly, instead of recognizing a set of anomalous actions that are commonly defined by previous dataset providers, in this work, we propose a contrastive learning approach to learn a metric to differentiate normal driving from anomalous driving. For this task, we introduce a new video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which contains normal driving videos together with a set of anomalous actions in its training set. In the test set of the DAD dataset, there are unseen anomalous actions that still need to be winnowed out from normal driving. Our method reaches 0.9673 AUC on the test set, demonstrating the effectiveness of the contrastive learning approach on the anomaly detection task. Our dataset, codes and pre-trained models are publicly available.