Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging

Abstract

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 (AIS) model of CAV-HDV interactions. Thus, the CAV learns the behavior of an incoming HDV using the AIS model and uses it to generate a control strategy for merging. First, we validate the efficacy of this framework on real-world data by using it to predict the behavior of an HDV in situations with other HDVs 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 AIS model learns to predict the future trajectory of the HDV using only observations. Subsequently, we generate safe merging control policies for a CAV when merging with HDVs that demonstrate a spectrum of driving behaviors, from aggressive to conservative. We establish the effectiveness of the proposed approach by performing numerical simulations.