In order to achieve precise motion control of the vehicle under various road surface conditions, it is essential to identify the road surface conditions accurately and quickly. For low-cost cameras and computing platforms, this paper proposes a reliable data-level fusion identification method that is robust to scenes with shadow. Based on deep-learning models, the candidate regions of interest with confidence level for each road surface condition are obtained. Besides, the fusion identification method based on improved Dempster-Shafer Evidence Theory is designed. The results of evaluation on specific dataset with shadow and anti-lock braking system using proposed method reveal that our proposed fusion method achieves superior performance with stable and accurate predictions.


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    Title :

    Reliable Identification of Road Surface Condition Considering Shadow Interference


    Contributors:
    Tian, Cheng (author) / Jin, Da (author) / Leng, Bo (author) / Xiong, Lu (author)


    Publication date :

    2021-09-19


    Size :

    2728172 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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