Windows of driver gaze data: How early and how much for robust predictions of driver intent?

Abstract

Previous work has demonstrated that distinct gaze patterns precede certain driving manoeuvres [1,2] and that they can be used to build an artificial neural network model which predicts a driver's intended manoeuvres [3,4]. This study seeks to move closer towards the goal of using gaze data in Advanced Driver Assistance Systems (ADAS) so that they can correctly infer the intentions of the driver from what is implied by the available incoming data. Drivers' gaze behaviour was measured in a dynamic driving simulator. The amount of gaze data required to make predictions that manoeuvres will occur and the reliablity of these predictions at increasing pre-manoeuvre times were investigated by using various sized windows of gaze data. The relative difficulty of predicting different manoeuvres and the accuracy of the models at different pre-manoeuvre times are discussed. © 2013 Springer-Verlag Berlin Heidelberg.