Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors

• URL: http://arxiv.org/abs/2502.00402v1
• Date: Sat, 01 Feb 2025 11:34:16 GMT
• Title: Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors
• Authors: Walter Zimmer, Ross Greer, Xingcheng Zhou, Rui Song, Marc Pavel, Daniel Lehmberg, Ahmed Ghita, Akshay Gopalkrishnan, Mohan Trivedi, Alois Knoll,
• Abstract summary: 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. We propose an accident detection framework that combines a rule-based approach with a learning-based one.
• Score: 6.420737230522813
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• Abstract: 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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