The evaluation of visual perception systems for autonomous vehicles (AVs) is challenging for developing AV technologies. Enlightened by current studies on human-like AV control, assessing the similarity of visual attention between AVs and human drivers becomes one of the potential means for the evaluation of AV technologies. This work proposes a human-like evaluation framework for the visual perception system of AVs, based on human visual attention benchmarks derived from expert human drivers' eye gaze data. We first create a perceptual benchmark by standardizing the gaze data from different expert human drivers. Then, we extract the features from the hidden layers of different AV perception algorithms. Finally, a human-like evaluation index is proposed to quantify the differences between chauffeured vehicles and AVs. The proposed human-like evaluation framework is of help for the development of AV system testing and evaluation by assessing the visual perception of AVs under various driving scenarios.


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    Titel :

    Human-Like Evaluation of Visual Perception System for Autonomous Vehicles Based on Human Visual Attention


    Beteiligte:
    Li, Chenhao (Autor:in) / Wang, Yijin (Autor:in) / Li, Kuang (Autor:in) / Fan, Qianhui (Autor:in) / Shen, Yu (Autor:in) / Ji, Yuxiong (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    584966 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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