The direction of a vehicle driver’s visual attention plays an essential role in the research on Advanced Driving Assistance Systems (ADAS) and autonomous vehicles. How a driver monitors the surrounding environment is at least partially descriptive of the driver’s situational awareness. While driver gaze is not explicitly related to head pose due to the interplay between head and eye movements, it may still provide an approximation of the visual attention that is sufficiently accurate for many applications. In this research, we propose a probabilistic method for describing the visual attention of drivers. This method applies a Gaussian Process Regression (GPR) technique that estimates the probability of the driver gaze direction, given head pose. We evaluate our model on real data collected during drives with an experimental vehicle in urban and suburban areas. Our experimental results show that 82.5% of drivers’ gaze lies within the 95% confidence interval predicted by our framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Probabilistic Model for Visual Driver Gaze Approximation from Head Pose Estimation


    Contributors:


    Publication date :

    2020-11-01


    Size :

    1552904 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Learning gaze biases with head motion for head pose-free gaze estimation

    Lu, F. / Okabe, T. / Sugano, Y. et al. | British Library Online Contents | 2014


    Model-based head pose-free gaze estimation for assistive communication

    Cristina, Stefania / Camilleri, Kenneth P. | British Library Online Contents | 2016


    Model-based head pose-free gaze estimation for assistive communication

    Cristina, Stefania / Camilleri, Kenneth P. | British Library Online Contents | 2016


    Model-based head pose-free gaze estimation for assistive communication

    Cristina, Stefania / Camilleri, Kenneth P. | British Library Online Contents | 2016