Driver’s Eye Fixation Prediction by Deep Neural Network

Abstract

The driving environment is a complex dynamic scene in which a driver’s eye fixation interacts with traffic scene objects to protect the driver from dangerous situations. Prediction of a driver’s eye fixation plays a crucial role in Advanced Driving Assistance Systems (ADAS) and autonomous vehicles. However, currently, no computational framework has been introduced to combine the bottom-up saliency map with the driver’s head pose and gaze direction to estimate a driver’s eye fixation. In this work, we first propose convolution neural networks to predict the potential saliency regions in the driving environment, and then use the probability of the driver gaze direction, given head pose as a top-down factor. We evaluate our model on real data gathered during drives in an urban and suburban environment with an experimental vehicle. Our analyses show promising results.