Identifying autonomous vehicles (AVs) (e.g., those with adaptive cruise control) from traffic stream benefits enhancing traffic safety, elevating roadway capacity, and assisting autonomous vehicle management. This study tests the feasibility of identifying AVs using externally observed vehicle trajectory information. Two learning-based models are utilized to conduct the identification with car-following trajectory information in a short time window as the input. Four car-following trajectory datasets involving AVs makes from different manufacturers are mixed to build a comprehensive identification model. Results show that AVs and human-driven vehicles (HVs) can be successfully identified with a very high accuracy, i.e., the long short-term memory network can correctly identify 98.17% of AVs and 94.14% of HVs. This verifies the feasibility of using existing infrastructure and economic technologies to identify AVs from HVs, which opens unprecedented data-driven opportunities to study and manage near-future mixed traffic.


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    Title :

    Automated Vehicle Identification in Mixed Traffic


    Contributors:
    Li, Qianwen (author) / Li, Xiaopeng (author) / Yao, Handong (author) / Liang, Zhaohui (author)


    Publication date :

    2021-09-19


    Size :

    387544 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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