  Highway merging scenarios featuring mixed traffic conditions pose significant
modeling and control challenges for connected and automated vehicles (CAVs)
interacting with incoming on-ramp human-driven vehicles (HDVs). In this paper,
we present an approach to learn an approximate information state model of
CAV-HDV interactions for a CAV to maneuver safely during highway merging. In
our approach, the CAV learns the behavior of an incoming HDV using approximate
information states before generating a control strategy to facilitate merging.
First, we validate the efficacy of this framework on real-world data by using
it to predict the behavior of an HDV in mixed traffic situations extracted from
the Next-Generation Simulation repository. Then, we generate simulation data
for HDV-CAV interactions in a highway merging scenario using a standard inverse
reinforcement learning approach. Without assuming a prior knowledge of the
generating model, we show that our approximate information state model learns
to predict the future trajectory of the HDV using only observations.
Subsequently, we generate safe control policies for a CAV while merging with
HDVs, demonstrating a spectrum of driving behaviors, from aggressive to
conservative. We demonstrate the effectiveness of the proposed approach by
performing numerical simulations.
