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Human-driven vehicles can amplify naturally occurring perturbations intraffic, leading to congestion and consequently increased fuel consumption,higher collision risks, and reduced capacity utilization. While previousresearch has highlighted that a fraction of Robot Vehicles (RVs) can mitigatethese issues, they often rely on simulations with simplistic, model-basedHuman-driven Vehicles (HVs) during car-following scenarios. Diverging from thistrend, in this study, we analyze real-world human driving trajectories,extracting a wide range of acceleration behaviors during car-following. We thenincorporate these behaviors in simulation where RVs from prior studies areemployed to mitigate congestion, and evaluate their safety, efficiency, andstability. Further, we also introduce a reinforcement learning based RV thatutilizes a congestion stage classifier neural network to optimize either"safety+stability" or "efficiency" in the presence of the diverse human drivingbehaviors. We evaluate the proposed RVs in two different mixed traffic controlenvironments at various densities, configurations, and penetration rates andcompare with the existing RVs.

Beyond Simulated Drivers: Evaluating the Impact of Real-World Car-Following in Mixed Traffic Control