# AVOID: Autonomous Vehicle Operation Incident Dataset Across the Globe

arXiv: https://arxiv.org/abs/2303.12889 · HTML: https://mlsanity.com/paper/2303.12889/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety](https://mlsanity.com/paper/2307.15311/) (2023)
- [SafeDriveRAG: Towards Safe Autonomous Driving with Knowledge Graph-based Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.21585/) (2025)
- [Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning](https://mlsanity.com/paper/2312.16620/) (2023)
- [My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data](https://mlsanity.com/paper/2504.17792/) (2025)
- [SAVeD: A First-Person Social Media Video Dataset for ADAS-equipped vehicle Near-Miss and Crash Event Analyses](https://mlsanity.com/paper/2512.17724/) (2025)

## Abstract (arXiv)

Crash data of autonomous vehicles (AV) or vehicles equipped with advanced driver assistance systems (ADAS) are the key information to understand the crash nature and to enhance the automation systems. However, most of the existing crash data sources are either limited by the sample size or suffer from missing or unverified data. To contribute to the AV safety research community, we introduce AVOID: an open AV crash dataset. Three types of vehicles are considered: Advanced Driving System (ADS) vehicles, Advanced Driver Assistance Systems (ADAS) vehicles, and low-speed autonomous shuttles. The crash data are collected from the National Highway Traffic Safety Administration (NHTSA), California Department of Motor Vehicles (CA DMV) and incident news worldwide, and the data are manually verified and summarized in ready-to-use format. In addition, land use, weather, and geometry information are also provided. The dataset is expected to accelerate the research on AV crash analysis and potential risk identification by providing the research community with data of rich samples, diverse data sources, clear data structure, and high data quality.