Feb. 9, 2024, 5:42 a.m. | Amin Karimi Monsefi Pouya Shiri Ahmad Mohammadshirazi Nastaran Karimi Monsefi Ron Davies Sobhan Moosavi

cs.LG updates on arXiv.org arxiv.org

Reducing traffic accidents is a crucial global public safety concern. Accident prediction is key to improving traffic safety, enabling proactive measures to be taken before a crash occurs, and informing safety policies, regulations, and targeted interventions. Despite numerous studies on accident prediction over the past decades, many have limitations in terms of generalizability, reproducibility, or feasibility for practical use due to input data or problem formulation. To address existing shortcomings, we propose CrashFormer, a multi-modal architecture that utilizes comprehensive (but …

accidents architecture cs.ai cs.lg enabling global key limitations multimodal prediction public public safety regulations risk safety studies traffic traffic safety

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