Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction

TL;DR

This paper presents a machine vision framework for analyzing and modeling driver gaze dynamics to predict driving maneuvers with high accuracy, aiding driver assistance and autonomous vehicle systems.

Contribution

It introduces a novel gaze behavior modeling approach that captures gaze dynamics through durations and frequencies, enabling early maneuver prediction.

Findings

Gaze patterns are consistent across different maneuvers.

Gaze dynamics can predict maneuvers hundreds of milliseconds in advance.

The framework effectively classifies gaze zones during freeway driving maneuvers.

Abstract

The study and modeling of driver's gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode. We developed a machine vision based framework to classify driver's gaze into context rich zones of interest and model driver's gaze behavior by representing gaze dynamics over a time period using gaze accumulation, glance duration and glance frequencies. As a use case, we explore the driver's gaze dynamic patterns during maneuvers executed in freeway driving, namely, left lane change maneuver, right lane change maneuver and lane keeping. It is shown that condensing gaze dynamics into durations and frequencies leads to recurring patterns based on driver activities. Furthermore, modeling these patterns show predictive powers in maneuver detection up to a few hundred milliseconds a priori.