In recent years, computer vision algorithms have become more powerful, which enabled technologies such as autonomous driving to evolve rapidly. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently (denoted as provident vehicle detection). For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively (react before the oncoming vehicle is directly visible). Using this experimental setting, the provident vehicle detection system’s time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently (detection based on observable visual cues the objects cause before they are visible) and further close the ...


    Access

    Download


    Export, share and cite



    Title :

    Provident vehicle detection at night for advanced driver assistance systems


    Contributors:

    Publication date :

    2022-01-01


    Remarks:

    Autonomous robots 47 (2023), Nr. 3 ; Autonomous robots



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    620 / 629




    Provident vehicle detection at night for advanced driver assistance systems

    Ewecker, Lukas / Asan, Ebubekir / Ohnemus, Lars et al. | TIBKAT | 2022

    Free access

    Provident vehicle detection at night

    Saralajew, Sascha / Gottfried Wilhelm Leibniz Universität Hannover / TEWISS - Technik und Wissen GmbH | GWLB - Gottfried Wilhelm Leibniz Bibliothek | 2023


    Provident Vehicle Detection at Night

    Saralajew, Sascha | TEWISS Verlag | 2023


    Provident vehicle detection at night

    Saralajew, Sascha / Gottfried Wilhelm Leibniz Universität Hannover / TEWISS - Technik und Wissen GmbH | TIBKAT | 2023


    PROVIDENT DETECTION OF VEHICLES AT NIGHT

    Oldenziel, Emilio / Ohnemus, Lars / Saralajew, Sascha | British Library Conference Proceedings | 2020