What Can Be Predicted from Six Seconds of Driver Glances?

TL;DR

This study investigates the predictive capabilities of a 6-second macro-glance sequence in real-world driving, demonstrating its potential to infer driver states and environmental factors using supervised learning.

Contribution

It introduces a large real-world dataset and shows that macro-glances can predict various driver and environment variables, advancing vision-based gaze estimation in practical settings.

Findings

Macro-glances can predict driver fatigue and distraction.

Glance data effectively infer environmental conditions.

Supervised learning achieves high accuracy in real-world scenarios.

Abstract

We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.