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

TL;DR

This paper introduces MR-IDM, a new lane-aware car-following model that improves realism in highway traffic simulations and autonomous vehicle merging scenarios by accounting for merging vehicles' influence.

Contribution

The paper presents MR-IDM, a novel reactive model that incorporates lateral awareness and merging vehicle effects, outperforming existing models in accuracy and stability.

Findings

MR-IDM achieves the lowest error in real-world data replication.

The model demonstrates smooth and stable driving behavior.

MR-IDM effectively accounts for merging vehicles in highway scenarios.

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.