Driving test scenarios from real-world driving data is considered as an effective solution for ICV on-road test. The main problem is how to select valid test scenarios from a large number of scenario snapshots. In this paper, a novel test scenario selecting method is proposed. Both the collision risk factor and the traffic factor are considered, and criticality evaluation factors for three typical applications - FCR, LCR and ICR, are defined. A LSTM-AE-Attention model is designed to identify critical scenarios. Experimental results show that the LSTM-AE-Attention based method has rapid convergence and acceptable accuracy while providing critical scenarios is reasonable.


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    Title :

    The Critical Scenario Extraction and Identification Method for ICV Testing


    Contributors:
    Han, Qingwen (author) / Yue, Jiao (author) / Ye, Lei (author) / Zeng, Lingqiu (author) / Long, Yang (author) / Wang, Yong (author)


    Publication date :

    2023-09-24


    Size :

    2593383 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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