  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.
