Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations

Enhancing simulation environments to replicate real-world driver behavior is essential for developing Autonomous Vehicle technology. While some previous works have studied the yielding reaction of lag vehicles in response to a merging car at highway on-ramps, the possible lane-change reaction of the lag car has not been widely studied. In this work we aim to improve the simulation of the highway merge scenario by including the lane-change reaction in addition to yielding behavior of main-lane lag vehicles, and we evaluate two different models for their ability to capture this reactive lane-change behavior. To tune the payoff functions of these models, a novel naturalistic dataset was collected on U.S. highways that provided several hours of merge-specific data to learn the lane change behavior of U.S. drivers. To make sure that we are collecting a representative set of different U.S. highway geometries in our data, we surveyed 50,000 U.S. highway on-ramps and then selected eight representative sites. The data were collected using roadside-mounted lidar sensors to capture various merge driver interactions. The models were demonstrated to be configurable for both keep-straight and lane-change behavior. The models were finally integrated into a high-fidelity simulation environment and confirmed to have adequate computation time efficiency for use in large-scale simulations to support autonomous vehicle development.

I. Introduction

MERGING at highway on-ramps has been a topic of interest in the transportation research community due to its significant impact on traffic flow, safety, and efficiency [1], [2]. Improving the efficiency of the highway on-ramp merging scenario can have a big impact on alleviating traffic congestion in cities [3], [4], [5]. Additionally, many drivers consider merging onto the highway to be a challenging and stressful scenario, especially during rush hour traffic, due to the need to negotiate with other drivers in both a short distance and time-sensitive manner [6].