This paper discusses the limitations of existing microscopic traffic models in accounting for the potential impacts of on-ramp vehicles on the car-following behavior of main-lane vehicles on highways. We first surveyed U.S. on-ramps to choose a representative set of on-ramps and then collected real-world observational data from the merging vehicle's perspective in various traffic conditions ranging from free-flowing to rush-hour traffic jams. Next, as our core contribution, we introduce a novel car-following model, called MR-IDM, for highway driving that reacts to merging vehicles in a realistic way. This proposed driving model can either be used in traffic simulators to generate realistic highway driving behavior or integrated into a prediction module for autonomous vehicles attempting to merge onto the highway. We quantitatively evaluated the effectiveness of our model and compared it against several other methods. We show that MR-IDM has the least error in mimicking the real-world data, while having features such as smoothness, stability, and lateral awareness.


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    Titel :

    MR-IDM - Merge Reactive Intelligent Driver Model: Towards Enhancing Laterally Aware Car-following Models


    Beteiligte:
    Holley, Dustin (Autor:in) / D'sa, Jovin (Autor:in) / Mahjoub, Hossein Nourkhiz (Autor:in) / Ali, Gibran (Autor:in) / Chalaki, Behdad (Autor:in) / Moradi-Pari, Ehsan (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    4384561 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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