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:自动驾驶汽车(AV)或配备高级驾驶辅助系统(ADAS)的汽车的崩溃数据是理解崩溃性质并增强自动化系统的关键信息。然而,大多数现有的崩溃数据来源要么受样本量限制,要么存在缺失或未经验证的数据。为了为AV安全研究社区做出贡献,我们介绍了一个开放的AV崩溃数据集AVOID。考虑了三种类型的车辆:高级驾驶系统(ADS)车辆、高级驾驶辅助系统(ADAS)车辆和低速自动班车。崩溃数据来自美国国家公路交通安全管理局(NHTSA)、加利福尼亚州机动车部(CA DMV)以及全球事件新闻报道,并经过手动验证和总结以供使用。此外,还提供土地利用、气象和几何信息。预计该数据集将通过提供样本丰富、数据来源多样、数据结构清晰、数据质量高的数据,加速AV崩溃分析和潜在风险识别的研究。