Rear-end collision crash is one of the most common accidents on the road. Accurate driving style recognition considering rear-end collision risk is crucial to design useful driver assistance systems and vehicle control systems. The purpose of this study is to develop a driving style recognition method based on vehicle trajectory data extracted from the surveillance video. First, three rear-end collision surrogates, Inversed Time to Collision (ITTC), Time-Headway (THW), and Modified Margin to Collision (MMTC), are selected to evaluate the collision risk level of vehicle trajectory for each driver. The driving style of each driver in training data is labelled based on their collision risk level using K-mean algorithm. Then, the driving style recognition model’s inputs are extracted from vehicle trajectory features, including acceleration, relative speed, and relative distance, using Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and statistical method to facilitate the driving style recognition. Finally, Supporting Vector Machine (SVM) is applied to recognize driving style based on the labelled data. The performance of Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) is also compared with SVM. The results show that SVM overperforms others with 91.7% accuracy with DWT feature extraction method.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data


    Beteiligte:
    Qingwen Xue (Autor:in) / Ke Wang (Autor:in) / Jian John Lu (Autor:in) / Yujie Liu (Autor:in)


    Erscheinungsdatum :

    2019




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving

    Yu, Hanyi / Huo, Shuning / Zhu, Mengran et al. | ArXiv | 2024

    Freier Zugriff


    Driving Style Recognition based on Ride Comfort Using a Hybrid Machine Learning Algorithm*

    del Campo, Ines / Asua, Estibalitz / Martinez, Victoria et al. | IEEE | 2018


    Driving style recognition using fuzzy logic

    Aljaafreh, Ahmad / Alshabatat, Nabeel / Najim Al-Din, Munaf S. | IEEE | 2012


    Construction method of deep reinforcement learning vehicle following model fused with driving style

    WANG KEYIN / SHI ZHEN / ZHANG JIANHUI et al. | Europäisches Patentamt | 2022

    Freier Zugriff