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

arXiv: https://arxiv.org/abs/2501.11758 · HTML: https://mlsanity.com/paper/2501.11758/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 79 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge Transfer](https://mlsanity.com/paper/2302.04527/) (2023)
- [Spiking-DD: Neuromorphic Event Camera based Driver Distraction Detection with Spiking Neural Network](https://mlsanity.com/paper/2407.20633/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Contextual Analysis of Driver-Facing and Dual-View Video Inputs for Distraction Detection in Naturalistic Driving Environments](https://mlsanity.com/paper/2512.20025/) (2025)

## Abstract (arXiv)

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.