Dual-view inputs boost distraction detection accuracy by 9.8% for SlowOnly but reduce it by 7.2% for SlowFast in naturalistic driving data, showing architecture-specific effects.
Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open challenges (2014–2021)
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A Contextual Analysis of Driver-Facing and Dual-View Video Inputs for Distraction Detection in Naturalistic Driving Environments
Dual-view inputs boost distraction detection accuracy by 9.8% for SlowOnly but reduce it by 7.2% for SlowFast in naturalistic driving data, showing architecture-specific effects.