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arXiv · 2305.12014

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

Dustin Holley·Jovin D'sa·Hossein Nourkhiz Mahjoub·Gibran Ali·Behdad Chalaki·Ehsan Moradi-Pari
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Abstract

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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Dustin Holley, Jovin D'sa, Hossein Nourkhiz Mahjoub, Gibran Ali, Behdad Chalaki, Ehsan Moradi-Pari. 2023-05-19. MR-IDM -- Merge Reactive Intelligent Driver Model: Towards Enhancing Laterally Aware Car-following Models. https://arxiv.org/abs/2305.12014

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