Heatmap-Based Method for Estimating Drivers' Cognitive Distraction

TL;DR

This paper introduces a novel heatmap-based approach using eye-gaze dispersion patterns and SVM classifiers to detect cognitive distraction in drivers, aiming to enhance road safety by identifying mental wandering.

Contribution

It proposes a new image-based representation of gaze behavior and demonstrates its effectiveness in classifying cognitive distraction with high accuracy.

Findings

Achieved 85.2% accuracy in detecting cognitive distraction

Drivers explore wider gaze areas when neutral, narrower when distracted

The method is effective even with small datasets

Abstract

In order to increase road safety, among the visual and manual distractions, modern intelligent vehicles need also to detect cognitive distracted driving (i.e., the drivers mind wandering). In this study, the influence of cognitive processes on the drivers gaze behavior is explored. A novel image-based representation of the driver's eye-gaze dispersion is proposed to estimate cognitive distraction. Data are collected on open highway roads, with a tailored protocol to create cognitive distraction. The visual difference of created shapes shows that a driver explores a wider area in neutral driving compared to distracted driving. Thus, support vector machine (SVM)-based classifiers are trained, and 85.2% of accuracy is achieved for a two-class problem, even with a small dataset. Thus, the proposed method has the discriminative power to recognize cognitive distraction using gaze information. Finally, this work details how this image-based representation could be useful for other cases of distracted driving detection.