Towards Achieving Cooperation Compliance of Human Drivers in Mixed Traffic

TL;DR

This paper proposes a refundable toll-based control framework to incentivize non-cooperative human drivers to follow socially optimal behaviors in mixed traffic, improving safety and efficiency.

Contribution

It introduces a novel cooperation compliance control scheme using refundable tolls to align human driver behavior with optimal traffic flow in mixed environments.

Findings

Enhanced compliance of human drivers with traffic rules.

Improved safety during lane-changing maneuvers.

Increased efficiency of traffic flow in simulations.

Abstract

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.