Abstract
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.
Abstract (translated)
无人驾驶车辆(AV)或装备先进驾驶辅助系统(ADAS)的车辆的事故数据是理解事故性质和提高自动化系统的关键技术信息。然而，大多数现有的事故数据来源都受到样本大小的限制，或者存在缺失或未验证的数据。为了为AV安全研究社区做出贡献，我们介绍了AVOID：一个开放的AV事故数据集。考虑了三种车辆类型：先进的驾驶系统(ADS)车辆、先进的驾驶辅助系统(ADAS)车辆和低速无人驾驶公交车。事故数据从全国高速公路交通安全管理局(NHTSA)、加利福尼亚州汽车管理局(CA DMV)和世界各地的新闻中收集，数据手动验证和总结，以 ready-to-use 格式呈现。此外，土地使用、天气和几何信息也提供了。预计数据集将加速AV事故分析和潜在风险识别的研究，通过提供丰富的样本、多样化的数据来源、清晰的数据结构和高质量的数据。
URL
https://arxiv.org/abs/2303.12889
