The driver monitoring system mainly monitors the real-time status of the driver through different data inputs such as cameras and vehicle motion behavior feedback. It issues warnings when the driver's attention is not focused, and even intervenes to control the vehicle when the driver enters an unconscious state. At present, there is relatively little research on the testing methods of driver distraction monitoring systems, and the selection process for drivers with different physical characteristics is not yet perfect, and the training system is not yet sound. A reasonable connection has not yet been established between the performance of drivers when distracted and the evaluation of vehicle dynamics and the performance of driver monitoring systems. There is relatively little research on the overall performance testing methods of driver distraction monitoring systems, especially on real vehicle testing methods. Based on this, the study proposes a distraction monitoring system testing method based on driver attention shift features to address the current issues of high randomness in driver selection, high variability in driver distraction actions, inconsistent testing methods, and difficulty in reproducing test results. The experimental results show that the research meets expectations, and the testing method can effectively ensure the consistency and reproducibility of the experiment, making an objective and accurate evaluation of the driver distraction monitoring system. The product performance is conducive to ensuring road driving safety.


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    Title :

    Research on testing technology of distraction monitoring system based on driver attention transfer features


    Contributors:

    Conference:

    International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China


    Published in:

    Proc. SPIE ; 13575


    Publication date :

    2025-04-28





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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