Abstract Driver intention recognition can enhance the driver-vehicle interaction by offering more intuitive assistance and automated driving support. Especially urban environments require fast reactions and hence assistance systems which act in accordance to driver’s intentions. Assistance should provide comfortably timed warnings only in situations when drivers really need this support and not in situations when the driver is already intending to react to a thread. Fraunhofer IAO developed an algorithm in the UR:BAN MV subproject VIE to detect driver’s intention to brake when passing a pedestrian. The Fraunhofer algorithm analyses eye gaze data in correspondence with pedal activity to judge the driver’s attention on the pedestrian and the readiness to brake. BMW implemented the algorithm in the UR:BAN KA subproject SVT in a research vehicle and combined it with an environmental analysis of the situation. In a test scenario the timing of a warning to the driver was adapted to the recognized intention to brake. Together with BMW’s pedestrian intention recognition algorithm, the driver intention recognition allows early warnings, while limiting the frequency of warnings to really relevant situations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Application of a Driver Intention Recognition Algorithm on a Pedestrian Intention Recognition and Collision Avoidance System




    Publication date :

    2017-06-23


    Size :

    18 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Combined Driver Distraction and Intention Algorithm for Maneuver Prediction and Collision Avoidance

    Gillmeier, Katharina / Schuettke, Tobias / Diederichs, Frederik et al. | IEEE | 2018


    Research on Ship Intention Recognition and Collision Avoidance Based on Bayesian Framework

    Tengfei, Wang / Jiyan, Xu / YaQiong, Lv et al. | Springer Verlag | 2025



    Driver Intention Recognition Based on Computer Vision

    Li, Xuehan / Chen, Huan'ming / Hua, Hang et al. | British Library Conference Proceedings | 2022


    Driver Intention Recognition Based on Computer Vision

    Hua, Hang / Chen, Huan'ming / Li, Xuehan et al. | SAE Technical Papers | 2022