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

翻译：人类驾驶车辆会放大交通中自然发生的扰动，导致拥堵，进而增加燃油消耗、提高碰撞风险并降低容量利用率。尽管先前研究已指出，一定比例的机器人车辆（RVs）能够缓解这些问题，但这些研究往往依赖于基于模型的简单化人类驾驶车辆（HVs）在跟驰场景中的模拟。与这一趋势不同，本研究分析了真实世界的人类驾驶轨迹，提取了跟驰过程中广泛的加速行为。随后，我们将这些行为纳入模拟中，并借助先前研究中用于缓解拥堵的RVs来评估其安全性、效率和稳定性。此外，我们还引入了一种基于强化学习的RV，该RV利用拥堵阶段分类器神经网络，在多样化的人类驾驶行为存在下优化“安全性+稳定性”或“效率”。我们在不同密度、配置和渗透率下的两种混合交通控制环境中评估了所提出的RVs，并与现有RVs进行了比较。