Towards Achieving Cooperation Compliance of Human Drivers in Mixed Traffic

We consider a mixed-traffic environment in transportation systems, where Connected and Automated Vehicles (CAVs) coexist with potentially non-cooperative Human-Driven Vehicles (HDVs). We develop a Cooperation Compliance Control (CCC) framework to incentivize HDVs to align their behavior with socially optimal objectives using a "refundable toll" scheme. This scheme achieves a desired compliance probability for all non-compliant HDVs through a feedback control mechanism combining global (social) with local (individual) components. We apply this scheme to the lane-changing problem, where a "Social Planner" provides references to the HDVs, measures their state errors, and induces cooperation compliance for safe lane-changing through a refundable toll approach. Simulation results are included to show the effectiveness of CCC in terms of improved compliance and lane-changing maneuver safety and efficiency when non-cooperative HDVs are present.