Assessing Driving Risk Level: Harnessing Deep Learning Hybrid Model With Intercity Bus Naturalistic Driving Data

Driving risk assessment is crucial for enhancing traffic safety, especially given the severe consequences of highway accidents. This study advances the field by developing a deep learning hybrid model for time series analysis to categorize driving risks into low, moderate, and high levels. By collec...

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Detalhes bibliográficos

Principais autores:	Wei-Hsun Lee, Che-Yu Chang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2025-01-01
coleção:	IEEE Access
Assuntos:	Driving risk assessment high-risk driving level predictor naturalistic driving data deep learning hybrid model static journey background information
Acesso em linha:	https://ieeexplore.ieee.org/document/10870206/
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