Self-DSNet: A Novel Self-ONNs Based Deep Learning Framework for Multimodal Driving Distraction Detection

<p dir="ltr">Distraction can cause delayed decision-making and slower awareness, posing significant risks in driving. For reliable driving systems, continuous monitoring of driver behavior is essential to mitigate the impact of distractions. Current strategies for distraction detecti...

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Bibliographic Details
Main Author:	Mamun Or Rashid (21976373) (author)
Other Authors:	Md. Mosarrof Hossen (21976376) (author), Mohammad Nashbat (17542194) (author), Mazhar Hasan-Zia (21399896) (author), Ali K. Ansaruddin Kunju (21323918) (author), Amith Khandakar (14151981) (author), Azad Ashraf (17541924) (author), Molla Ehsanul Majid (21976379) (author), Saad Bin Abul Kashem (19117156) (author), Muhammad E. H. Chowdhury (14150526) (author)
Published:	2025
Subjects:	Engineering Automotive engineering Biomedical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Human-centred computing Machine learning Driving distraction detection Self-organizing neural network (Self-ONN) Multimodal data Driver behavior monitoring Vehicle dynamics Vehicles Feature extraction Drives Driver behavior Biomedical monitoring Data models Brain modeling Accidents
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Self-DSNet: A Novel Self-ONNs Based Deep Learning Framework for Multimodal Driving Distraction Detection

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