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

arXiv: https://arxiv.org/abs/2305.12014 · HTML: https://mlsanity.com/paper/2305.12014/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2023.

## Built on by (most cited first, 2 of 2)

- [MR-LDM -- The Merge-Reactive Longitudinal Decision Model: Game Theoretic Human Decision Modeling for Interactive Sim Agents](https://mlsanity.com/paper/2507.12494/) (2025)
- [Modeling Electric Vehicle Car-Following Behavior: Classical vs Machine Learning Approach](https://mlsanity.com/paper/2510.24085/) (2025)

## Abstract (arXiv)

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.