The Intelligent Driver Model (IDM) is a microscopic, time continuous car following model for the simulation of freeway and urban traffic. Its popularity is grounded in its simplicity and its capacity to describe both single vehicle velocity profiles as well as collective traffic behavior. Nevertheless, it lacks a series of properties that would be desirable for more realistic agent models. In this paper, as an alternative and improvement to the IDM, we propose the Foresighted Driver Model (FDM), which assumes that a driver acts in a way that balances predictive risk (e.g. due to possible collisions along its route) with utility (e.g. the time required to travel, smoothness of ride, etc.). Based on a risk concept developed for full behavior planning, we introduce driver model equations from the assumption that a driver will mainly try to avoid risk maxima in time and space. We show how such a model can be used to simulate driving behavior similar to full behavior planning models and which generalizes and reaches beyond the IDM modeling scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    The Foresighted Driver Model


    Contributors:


    Publication date :

    2015-06-01


    Size :

    1272195 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Complex Lane Change Behavior in the Foresighted Driver Model

    Julian, Eggert / Damerow, Florian | IEEE | 2015


    A Foresighted Driver Model derived from Integral Expected Risk

    Eggert, Julian / Mueller, Fabian | IEEE | 2019


    Foresighted People Finding and Following

    Bayoumi, AbdElMoniem | BASE | 2018

    Free access

    Communication Systems for Cooperative Foresighted Driving

    Schubert, E. / Reck, L. / Graf, J. et al. | British Library Conference Proceedings | 1994


    Foresighted medical resources allocation during an epidemic outbreak

    Pan, Yuqing / Cheng, T.C.E. / He, Yuxuan et al. | Elsevier | 2022