Towards Achieving Cooperation Compliance of Human Drivers in Mixed Traffic

TL;DR

The paper tackles safety and efficiency in mixed traffic by introducing a Cooperation Compliance Control framework that uses refundable token-based tolls to nudge non-cooperative HDVs toward social optimality. A Social Planner provides references and measures deviations, while a dual global/local control scheme updates a global toll and individual penalties, shaping HDV compliance via a probabilistic model $P_i(k+1)=p(w_q q_i+w_c C(k)+w_i c_i(k))$. The framework is instantiated in a lane-changing problem, where longitudinal and lateral maneuvers are optimized under safety constraints, and HDV behavior is continuously adjusted through periodic re-optimization and penalties. Simulation results show improved compliance, safer merges, and reduced travel time and energy consumption, with the refundable toll mechanism ensuring fairness and convergence toward higher system-wide efficiency in mixed traffic.

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.

Figures (5)

• Figure 1: Cooperation Compliance Control (CCC) framework. Compliant agents are CAVs that provide real-time feedback to the SP. Non-compliant agents are HDVs whose probability of compliance is controlled through refundable tolls.
• Figure 2: The basic lane-changing problem. The orange vehicles are HDVs, and the green vehicles are CAVs. The gray vehicle is a slow-moving vehicle.
• Figure 3: The longitudinal position of CAV $C$ and all vehicles $i\in S(t_0)=\{1,2,3,4,5\}$ during the entire maneuver.
• Figure 4: The changing of compliance behavior, global controller (penalty), and individual controller for the two HDVs 1 and 4 in the fast lane during the entire lane-changing maneuver for CAV $C$.
• Figure 5: The change of the compliance probability for HDVs 1 and 4 in a lane-changing maneuver.

Theorems & Definitions (1)

• Remark 1