Predicting driver behaviour at intersections based on driver gaze and traffic light recognition

Abstract

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.

Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates

“…Until recently, the majority of driving observation frameworks comprised a manual feature extraction step followed by a classification module (for a thorough overview see [21]). The constructed feature vectors are often derived from hand-and body-pose [2], [3], [6], [7], [38], [39], facial expressions and eye-based input [40], [41], and head pose [42], [43], but also foot dynamics [44], detected objects [6], and physiological signals [45] have been considered. Classification approaches are fairly similar to the ones used in standard video classification, with LSTMs [3], [4], SVMs [2], [46], random forests [47] or HMMs [4], and graph neural networks [7], [48] being popular choices.…”