Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913673912516608 |
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| author | Zimmer, Walter Greer, Ross Zhou, Xingcheng Song, Rui Pavel, Marc Lehmberg, Daniel Ghita, Ahmed Gopalkrishnan, Akshay Trivedi, Mohan Knoll, Alois |
| author_facet | Zimmer, Walter Greer, Ross Zhou, Xingcheng Song, Rui Pavel, Marc Lehmberg, Daniel Ghita, Ahmed Gopalkrishnan, Akshay Trivedi, Mohan Knoll, Alois |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors Zimmer, Walter Greer, Ross Zhou, Xingcheng Song, Rui Pavel, Marc Lehmberg, Daniel Ghita, Ahmed Gopalkrishnan, Akshay Trivedi, Mohan Knoll, Alois Computer Vision and Pattern Recognition 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. |
| title | Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.00402 |