Probabilistic microscopic traffic models provide a statistical representation of interactive behavior between traffic participants. They are crucial for the validation of automotive safety systems that make decisions based on surrounding traffic. The construction of such models by hand is error-prone and difficult to extend to the complete diversity of human behavior. This paper describes a methodology for microscopic traffic model construction based on a Bayesian statistical framework connected to real-world data and applies it to learning models for free-flow, car following, and lane-change behaviors on highways. The evolution of traffic scenes is represented by a generative model learned for individual vehicles that captures their response to other traffic participants as well as the road structure. Our evaluation shows realistic behaviors over a four second horizon. A complete implementation is available online.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Probabilistic Framework for Microscopic Traffic Propagation


    Contributors:


    Publication date :

    2015-09-01


    Size :

    216591 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






    A Hybrid Submicroscopic-Microscopic Traffic Flow Simulation Framework

    Mullakkal-Babu, Freddy Antony / Wang, Meng / van Arem, Bart et al. | IEEE | 2021


    A probabilistic framework for traffic data quality

    Ebendt, Rüdiger / Neumann, Thorsten | German Aerospace Center (DLR) | 2018

    Free access