This work introduces and evaluates a model for predicting driver behaviour, namely turns or proceeding straight, at traffic light intersections from driver three-dimensional gaze data and traffic light recognition. Based on vehicular data, this work relates the traffic light position, the driver's gaze, head movement, and distance from the centre of the traffic light to build a model of driver behaviour. The model can be used to predict the expected driver manoeuvre 3 to 4 s prior to arrival at the intersection. As part of this study, a framework for driving scene understanding based on driver gaze is presented. The outcomes of this study indicate that this deep learning framework for measuring, accumulating and validating different driving actions may be useful in developing models for predicting driver intent before intersections and perhaps in other key-driving situations. Such models are an essential part of advanced driving assistance systems that help drivers in the execution of manoeuvres.
Predicting driver behaviour at intersections based on driver gaze and traffic light recognition
IET Intelligent Transport Systems ; 14 , 14 ; 2083-2091
2021-02-22
9 pages
Article (Journal)
Electronic Resource
English
human factors , gaze tracking , road vehicles , traffic light intersections , driver behaviour prediction , traffic light position , advanced driving assistance systems , head movement , road traffic , driver three-dimensional gaze data , expected driver manoeuvre , driver intent prediction , traffic light recognition , deep learning (artificial intelligence) , driving scene understanding , deep learning framework , driver information systems
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