Deep learning approaches for driver distraction detection using driver facing cameras: literature review and empirical study using cnn classifiers on a 100-driver image dataset

Abstract

Distracted driving contributes to thousands of fatalities and injuries globally. According to India’s Ministry of Road Transport and Highways (MoRTH), distraction-related behaviors such as rear-end and off-road collisions accounted for nearly one-fourth of all traffic incidents in 2022. The U.S. National Highway Traffic Safety Administration (NHTSA) reported 3,275 deaths and over 324,000 injuries from distraction-related crashes in 2023. In Europe, the European Road Safety Observatory (ERSO) observed handheld phone use by drivers in up to 9.4% of vehicles across member states, with self-reported texting rates reaching 53%. Despite numerous studies and surveys on driver distraction detection, existing literature remains fragmented, often combining multiple sensor modalities or distraction with related driver states such as fatigue. Prior empirical efforts also lack a unified benchmarking strategy to assess model generalization under shifts in viewpoint or spectral input. This paper presents a focused survey and empirical study of visiononly distraction detection using deep learning models applied to driver-facing camera inputs. It introduces a conceptual model linking behavioral cues to cognitive distraction, defines the visionbased Driver Distraction Detection (vDDD) system with alert logic, and develops structured taxonomies of datasets, architectures, and learning strategies. Using the 100-Driver dataset, the empirical study evaluates 26 CNN classifiers under 64 crossdomain configurations, systematically analyzing generalization across modality and camera view changes. Results show that frontal RGB-trained models generalize better than their NIRtrained counterparts and that lightweight models trade off accuracy under rare class scenarios for faster inference. The study establishes the vDDD paradigm as a vision-based behavioral modeling approach for distraction detection using driver-facing camera data. It outlines future research directions in spectrumaligned augmentation, attention modeling, and lightweight visuallanguage fusion, emphasizing deployment-focused strategies such as quantization, contrastive learning, and progressive fine-tuning.