# Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1901.09097 · HTML: https://mlsanity.com/paper/1901.09097/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2019.

## Built on by (most cited first, 26 of 26)

- [AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception](https://mlsanity.com/paper/2307.13933/) (2023)
- [Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos](https://mlsanity.com/paper/2306.10159/) (2023)
- [Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition](https://mlsanity.com/paper/2101.06636/) (2021)
- [Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition](https://mlsanity.com/paper/2110.12183/) (2021)
- [ViT-DD: Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection](https://mlsanity.com/paper/2209.09178/) (2022)
- [Dynamic Conditional Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/2211.11579/) (2022)
- [Real-Time Driver Monitoring Systems through Modality and View Analysis](https://mlsanity.com/paper/2210.09441/) (2022)
- [(Safe) SMART Hands: Hand Activity Analysis and Distraction Alerts Using a Multi-Camera Framework](https://mlsanity.com/paper/2301.05838/) (2023)
- [Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition](https://mlsanity.com/paper/2301.12592/) (2023)
- [Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition](https://mlsanity.com/paper/2101.06634/) (2021)
- [A Computer Vision-Based Approach for Driver Distraction Recognition using Deep Learning and Genetic Algorithm Based Ensemble](https://mlsanity.com/paper/2107.13355/) (2021)
- [Detection of Distracted Driver using Convolution Neural Network](https://mlsanity.com/paper/2204.03371/) (2022)
- [Explainable Interface for Human-Autonomy Teaming: A Survey](https://mlsanity.com/paper/2405.02583/) (2024)
- [BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization](https://mlsanity.com/paper/2405.06049/) (2024)
- [Sage Deer: A Super-Aligned Driving Generalist Is Your Copilot](https://mlsanity.com/paper/2505.10257/) (2025)
- [Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention](https://mlsanity.com/paper/2304.06370/) (2023)
- [Using Visual and Vehicular Sensors for Driver Behavior Analysis: A Survey](https://mlsanity.com/paper/2308.13406/) (2023)
- [Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing Supervision](https://mlsanity.com/paper/2310.05202/) (2023)
- [PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformer](https://mlsanity.com/paper/2312.14577/) (2023)
- [Transformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition](https://mlsanity.com/paper/2403.06577/) (2024)
- [DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification](https://mlsanity.com/paper/2409.05587/) (2024)
- [Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning](https://mlsanity.com/paper/2411.13181/) (2024)
- [Contextual Intelligent Decisions: Expert Moderation of Machine Outputs for Fair Assessment of Commercial Driving](https://mlsanity.com/paper/2202.09816/) (2022)
- [Density-Guided Label Smoothing for Temporal Localization of Driving Actions](https://mlsanity.com/paper/2403.06616/) (2024)
- [V-SenseDrive: A Privacy-Preserving Road Video and In-Vehicle Sensor Fusion Framework for Road Safety & Driver Behaviour Modelling](https://mlsanity.com/paper/2509.18187/) (2025)
- [A Two-stage Transformer Framework for Temporal Localization of Distracted Driver Behaviors](https://mlsanity.com/paper/2603.21048/) (2026)

## Abstract (arXiv)

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.