Finding edge hazardous scenarios which appear very infrequently in the dataset than common hazardous scenarios is essential for implementing scenario-based testing of autonomous driving systems(ADs). However, it is difficult to evaluate the rarity of dynamic scenarios with huge scenario space high-dimensional time series, making it difficult to search for edge hazardous scenarios quickly. To solve this problem, this paper proposes a Semi-supervised anomaly detection method combining MiniRocket and DAGMM(Semi-MiniRocket-GMM, SRG), which treats edge hazardous scenarios as anomalous samples of common hazardous scenarios. SRG uses a small number of samples of common hazardous scenarios to guide interpretable feature extraction and clustering of a large amount of high-dimensional unlabeled temporal data and finds rarer edge hazardous scenarios based on anomaly evaluation to improve the coverage of test scenarios. The method is validated in the open-source natural driving dataset HighD. Compared with DAGMM, the SRG method can find edge hazardous lane change scenarios more quickly and accurately with a few samples of hazardous scenarios. The SRG method aimed at discovering edge hazardous scenarios can both guide the direction of generating scenarios and speed up the testing process.


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    Titel :

    A Fast Search Method for Edge Hazardous Scenarios Based on Semi-Supervised Anomaly Detection


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Li, Fang (Autor:in) / Wang, Lifang (Autor:in) / Li, Mengyu (Autor:in) / Guo, Zihan (Autor:in)

    Kongress:

    SAE 2023 Intelligent and Connected Vehicles Symposium ; 2023



    Erscheinungsdatum :

    2023-12-20




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch


    Schlagwörter :


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