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

Understand

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.