Abstract
This study delves into leveraging Controller Area Network (CAN) data to detect and analyze abnormal driving patterns, underlining its significant role in bolstering road safety measures. By meticulously examining the comprehensive data supplied by the CAN system, which encapsulates real-time inputs from many vehicle sensors and mechanisms, this research marks a pivotal stride in the domain of vehicular safety and intelligent transport networks. The investigation elucidates on categorizing three specific types of unusual driving conduct, showcasing the accuracy and dependability of utilizing CAN data for such purposes. This methodology is a critical breakthrough in crafting instantaneous monitoring systems for erratic driving behavior, aiming to foster safer driving environments.
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Acknowledgments
This work was supported by Institute for Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-01197, Convergence security core talent training business (Soon Chun Hyang University)) and supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A2001810).
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Islam, M.R., Yusupov, K., Batzorig, M., Oh, I., Yim, K. (2024). Enhancing Road Safety with In-Vehicle Network Abnormal Driving Behavior Detection. In: Barolli, L. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 214. Springer, Cham. https://doi.org/10.1007/978-3-031-64766-6_9
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DOI: https://doi.org/10.1007/978-3-031-64766-6_9
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