The modeling of car-following behavior is an attractive research topic in traffic simulation and intelligent transportation. The driver plays an important role in car following but is ignored by most car-following models. This paper presents a novel car-following driver model, which can retain aspects of human driving styles. First, simulated car-following data are generated by using the speed control driver model and the real-world driving behavior data if the real-world car-following data are not available. Then, the car-following driver model is established by imitating human driving maneuver during real-world car following. This is accomplished by using a neural network-based learning control paradigm and car-following data. Finally, the FTP-72 driving cycle is borrowed as the speed profile of the leading vehicle for the model test. The driving style is quantitatively analyzed by AESD. The results show that the proposed car-following driver model is capable of retaining the naturalistic driving styles while well accomplishing the car-following task with the error of relative distance mostly less than 5 meters for every driving styles.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Car-Following Driver Model Capable of Retaining Naturalistic Driving Styles


    Beteiligte:
    Jie Hu (Autor:in) / Sheng Luo (Autor:in)


    Erscheinungsdatum :

    2020




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Application of Naturalistic Driving Data to Modeling of Driver Car-Following Behavior

    Sangster, John / Rakha, Hesham / Du, Jianhe | Transportation Research Record | 2013



    Driver Head Pose Detection From Naturalistic Driving Data

    Chai, Weiheng / Chen, Jiajing / Wang, Jiyang et al. | IEEE | 2023


    Predicting Driver Intent from Models of Naturalistic Driving

    Bender, Asher / Ward, James R. / Worrall, Stewart et al. | IEEE | 2015