A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection

TL;DR

This review comprehensively analyzes how different data modalities and machine learning techniques impact the effectiveness of distracted driving detection systems, highlighting the advantages of multimodal approaches for improved accuracy.

Contribution

It provides a systematic categorization and evaluation of ML and DL methods across various data modalities, identifying the most effective strategies for distracted driving detection.

Findings

Multimodal systems outperform single-modal approaches in accuracy.

Deep learning techniques show significant promise in complex distraction detection.

Visual and sensory data modalities are most effective for real-time detection.

Abstract

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.