  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 framework to incentivize HDVs to align their behavior with
socially optimal objectives using a ``refundable toll'' scheme so as to achieve
a desired compliance probability for all non-compliant HDVs through a feedback
control mechanism combining global 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 our cooperation
compliance controller in terms of improved compliance and lane-changing
maneuver safety and efficiency when non-cooperative HDVs are present.
