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.

翻译：自动驾驶车辆（AV）或配备高级驾驶辅助系统（ADAS）的车辆碰撞数据是理解碰撞本质、增强自动化系统的关键信息。然而，现有碰撞数据源大多受样本量限制，或存在数据缺失及未经验证的问题。为支持自动驾驶车辆安全研究，我们提出了AVOID：一个开放的自动驾驶车辆碰撞数据集。该数据集涵盖三类车辆：高级驾驶系统（ADS）车辆、高级驾驶员辅助系统（ADAS）车辆以及低速自动驾驶穿梭车。碰撞数据收集自美国国家公路交通安全管理局（NHTSA）、加利福尼亚州机动车辆管理局（CA DMV）及全球事故新闻报道，所有数据均经人工验证并以即用格式汇总。此外，还提供土地利用、天气和几何信息。该数据集通过向研究社区提供样本丰富、来源多样、结构清晰、质量高的数据，有望加速自动驾驶车辆碰撞分析与潜在风险识别的研究。