Highlights Using stacked autoencoder to obtain embedding features from high-dimensional data. Identifying typical car-to-powered two-wheelers scenarios. Six typical scenarios are obtained with the proposed processes. Forming a more detailed scenario description for all typical scenarios.

    Abstract Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-world crash scenarios can be used as critical scenarios to test the safety performance of AVs. As one of the dominant types of traffic crashes, the car to powered-two-wheelers (PTWs) crash results in a higher possibility of fatality than ordinary car-to-car crashes. Generally, typical testing scenarios are chosen according to the subjective understanding of the safety experts with limited static features of crashes (e.g., geometric features, weather). This study introduced a novel method to identify typical car-to-PTWs crash scenarios based on real-world crashes with dynamic pre-crash features investigated from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. First, we present crash data collection and construction methods of the CIMSS-TA database to construct testing scenarios. Second, the stacked autoencoder methods are used to learn and obtain embedded features from the high-dimensional data. Third, the extracted features are clustered using k -means clustering algorithm, and then the clustering results are interpreted. Six typical car-to-PTWs scenarios are obtained with the proposed processes. This study introduces a typical high-risk scenario construction method based on deep embedded clustering. Unlike existing researches, the proposed method eliminates the negative impacts of manually selecting clustering variables and provides a more detailed scenario description. As a result, the typical scenarios obtained from AV testing are more robust.


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    Titel :

    Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing


    Beteiligte:
    Zhou, Rui (Autor:in) / Huang, Helai (Autor:in) / Lee, Jaeyoung (Autor:in) / Huang, Xiangzhi (Autor:in) / Chen, Jiguang (Autor:in) / Zhou, Hanchu (Autor:in)


    Erscheinungsdatum :

    2023-07-07




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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