Towards Achieving Cooperation Compliance of Human Drivers in Mixed Traffic
<|reference_start|>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 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.<|reference_end|>
@article{li2024towards,
    title={Towards Achieving Cooperation Compliance of Human Drivers in Mixed
  Traffic},
    author={Anni Li and Christos G. Cassandras},
    journal={arXiv preprint arXiv:2405.17594},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17594},
    primaryClass={eess.SY cs.SY}
}