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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    Titel :

    The Critical Scenario Extraction and Identification Method for ICV Testing


    Beteiligte:
    Han, Qingwen (Autor:in) / Yue, Jiao (Autor:in) / Ye, Lei (Autor:in) / Zeng, Lingqiu (Autor:in) / Long, Yang (Autor:in) / Wang, Yong (Autor:in)


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    2593383 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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