Agent-Based Evolutionary Dynamics for Mixed Autonomy Weaving Ramps

Sheryl Paul; Kexin Wang; Ruolin Li; Jyotirmoy V. Deshmukh

cs.MASep 29, 2026

Agent-Based Evolutionary Dynamics for Mixed Autonomy Weaving Ramps

Authors: Sheryl Paul, Kexin Wang, Ruolin Li, Jyotirmoy V. Deshmukh

Organizations: University of Southern California, Los Angeles, CA, USA.

Abstract

Existing models of mixed-autonomy weaving ramps characterize how altruistic connected and automated vehicles (CAVs) can improve traffic efficiency at the population level, but provide limited insight into how such behavior emerges from decentralized vehicle interactions or how it is affected by finite populations, heterogeneous preferences, and imperfect information. We develop an agent-based model of a macroscopic weaving-ramp framework in which individual vehicles adapt their lane choices using an evolutionary game-theoretic update rule and altruism-based objectives providing a microscopic interpretation of the original Wardrop model. We prove convergence of the decentralized dynamics to the unique equilibrium predicted by the macroscopic theory. Beyond reproducing aggregate equilibrium behavior, the framework enables the study of deployment-level questions that cannot be addressed by static analysis. Simulation results demonstrate close agreement with the macroscopic predictions while revealing how convergence rates, adaptation to changing traffic conditions, heterogeneous altruism levels among CAVs, and imperfect state information influence system performance and the distribution of altruistic burden across vehicles. These results provide a bridge between equilibrium traffic theory and decentralized mixed-autonomy deployment.

Figures & tables

Figure 1: HDV-only ABM, closed-form Wardrop equilibrium, and independent SUMO simulation.

Figure 2: Analytic mixed-population equilibrium (left) and ABM results (right), including thresholds $p_{1}$ and $p_{2}$ .

Figure 3: Social cost for homogeneous and heterogeneous CAV fleets. Profiles with matched $\bar{\theta}_{\mathrm{eff}}$ have similar equilibria.

Figure 4: Steps to equilibrium over $(p,\eta_{\mathrm{CAV}})$ at $\theta=\pi/2$ ; dotted lines indicate $p_{1}$ and $p_{2}$ .

Figure 5: ABM tracking of sinusoidal demand with periods $40$ and $200$ for $p>p_{2}$ .

Figure 6: Per-agent cost distributions (left) and Gini coefficients (right) across CAV fleet profiles.

Figure 7: Social cost under perfect information, memoryless estimation, and cooperative belief sharing.

Figure 8: Recovery after a demand-composition shock across CAV penetration rates.

Figure 9: Post-shock recovery of social cost under different forgetting factors $\lambda$ ; the shock occurs at $t=400$ .

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Sep 30, 2026, math.OCJ/K move · Enter open · S save

Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$ -Potential Game Framework

Anran Hu, Zhexin Wang, Yufei Zhang, Xuan Di

Department of Industrial Engineering and Operations Research, Columbia University · Department of Mathematics, Imperial College London, London, UK · Department of Civil Engineering and Engineering Mechanics, Data Science Institute, Columbia University

Decentralized Autonomous Organization

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We study noncooperative multi-vehicle games among heterogeneous autonomous vehicles, where each vehicle adopts a decentralized closed-loop policy based on its own state, and optimizes an objective that depends on other vehicles through potentially asymmetric interaction weights. We develop an

α

-potential game framework that reduces the computation of an approximate Nash equilibrium (NE) to the minimization of a single auxiliary

α

-potential function. We explicitly construct this

α

-potential, establish the existence of its minimizers, and characterize the equilibrium approximation error

α

in terms of interaction asymmetry. We further introduce vehicle-specific scaling to reduce the effective interaction asymmetry, thereby tightening the equilibrium approximation and, in important cases, recovering an exact NE despite asymmetric interactions. We also derive social-efficiency guarantees for the potential-selected policies, revealing how the interaction structure shapes worst-case efficiency. Numerical experiments demonstrate the flexibility of the framework in capturing heterogeneous vehicle interactions, collision and obstacle avoidance, lane changing and overtaking under different traffic configurations, and priority-based intersection crossing.

Abstract

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