A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection
It provides guidance for researchers and practitioners to improve road safety by identifying effective detection strategies, though it is incremental as a review paper.
This systematic review analyzed machine and deep learning techniques for distracted driving detection across various data modalities, finding that multimodal systems often yield higher accuracy than single-modal approaches.
Distracted driving remains a significant global challenge with severe human and economic repercussions, demanding improved detection and intervention strategies. While previous studies have extensively explored single-modality approaches, recent research indicates that these systems often fall short in identifying complex distraction patterns, particularly cognitive distractions. This systematic review addresses critical gaps by providing a comprehensive analysis of machine learning (ML) and deep learning (DL) techniques applied across various data modalities - visual,, sensory, auditory, and multimodal. By categorizing and evaluating studies based on modality, data accessibility, and methodology, this review clarifies which approaches yield the highest accuracy and are best suited for specific distracted driving detection goals. The findings offer clear guidance on the advantages of multimodal versus single-modal systems and capture the latest advancements in the field. Ultimately, this review contributes valuable insights for developing robust distracted driving detection frameworks, supporting enhanced road safety and mitigation strategies.