用路侧传感器数据提升高速公路事故检测速度，助力减少伤亡。
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors
- 结合规则与学习方法，实现高效事故检测。
- 构建包含29万标注框的高速事故数据集，帧率10Hz。
- 适合智能交通、自动驾驶安全系统研发人员参考。
道路交通伤害是5至29岁人群的主要死因，每年导致约119万人死亡。减少伤亡需应对超速、酒驾、分心等人为错误，同时加快事故识别和医疗响应。本文提出一种融合规则与学习的事故检测框架，并构建一个真实高速公路事故数据集，涵盖48,144帧、10 Hz采样率，由四台路侧摄像头与激光雷达采集，包含294,924个2D标注框、93,012个3D标注框及目标跟踪ID，覆盖十类物体，以OpenLABEL格式发布。实验验证了该方法的可靠性。
原文摘要 · Abstract (English)
Road traffic injuries are the leading cause of death for people aged 5-29, resulting in about 1.19 million deaths each year. To reduce these fatalities, it is essential to address human errors like speeding, drunk driving, and distractions. Additionally, faster accident detection and quicker medical response can help save lives. We propose an accident detection framework that combines a rule-based approach with a learning-based one. We introduce a dataset of real-world highway accidents featuring high-speed crash sequences. It includes 294,924 labeled 2D boxes, 93,012 labeled 3D boxes, and track IDs across 48,144 frames captured at 10 Hz using four roadside cameras and LiDAR sensors. The dataset covers ten object classes and is released in the OpenLABEL format. Our experiments and analysis demonstrate the reliability of our method.
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