Lane Change (LC) detection is the foundation of LC studies using real-world data. Most current studies use rule-based methods for LC detection from experimental data. In this study, we propose a learning-based method to detect LC using large-scale naturalistic driving data. The dataset is analyzed using big data analytics method, and the potential LC maneuvers are extracted. The LC detection is reformulated as a one-class classification problem, and an autoencoder-based anomaly detection method is developed to solve it. The proposed method is robust to data noises and can achieve better detection performance than the one-class Support Vector Machine (SVM). This work lays the groundwork for future LC studies, such as driving behaviour modeling and traffic safety solutions.


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    Titel :

    Lane Change Detection Using Naturalistic Driving Data


    Beteiligte:
    Guo, Hongyu (Autor:in) / Xie, Kun (Autor:in) / Keyvan-Ekbatani, Mehdi (Autor:in)


    Erscheinungsdatum :

    2021-06-16


    Format / Umfang :

    5449475 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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