For future Advanced Driver Assistance Systems (ADAS), knowledge about what the driver perceived in his surrounding environment is important to estimate the driver's situation awareness. This estimate can then be used for example to adapt the systems' warning and intervention strategies according to the driver's needs. We propose a Dynamic Bayesian Network (DBN) which operates in the ground plane at pixel level and simultaneously tracks two gaze motion models to model the driver's focus of attention. We introduce a new time variant transition probability for motion hypotheses of fixations and saccades combining spatial and temporal domain motivated by human gaze motion characteristics. For environment perception, we solely rely on series sensors, while for gaze tracking, a commercial eye tracker is employed. Our system efficiently smooths the measured gaze target point during estimated fixations while preserving the characteristics of saccadic jump behavior. Thereby, the driver's gaze target in the world is effectively extracted.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver's gaze prediction in dynamic automotive scenes


    Contributors:


    Publication date :

    2017-10-01


    Size :

    618202 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction

    Martin, Sujitha / Vora, Sourabh / Yuen, Kevan et al. | ArXiv | 2018

    Free access

    Dynamics of Driver's Gaze: Explorations in Behavior Modeling and Maneuver Prediction

    Martin, Sujitha / Vora, Sourabh / Yuen, Kevan et al. | IEEE | 2018


    Study on Driver's Unsafe Gaze Behavior Detection Technology

    Wang, Peng / Liu, Zhi-Qiang | Tema Archive | 2013


    Multi-Hypothesis Multi-Model Driver's Gaze Target Tracking

    Schwehr, Julian / Willert, Volker | IEEE | 2018


    Gaze dynamics with spatiotemporal guided feature descriptor for prediction of driver’s maneuver behavior

    Yan, Qiunv / Zhang, Weiwei / Hu, Wenhao et al. | SAGE Publications | 2021