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.

人工驱动车辆导致交通拥堵，增加燃油消耗、碰撞风险和减少容量利用率。本研究通过分析真实世界中的人类驾驶轨迹，提取了在车跟随过程中的加速行为，并将这些行为与之前研究中的自动驾驶车辆结合起来，以减少拥堵并评估其安全性、效率和稳定性。此外，还引入了基于强化学习的自动驾驶车辆，利用拥堵阶段分类神经网络来优化 “安全 + 稳定” 或 “效率”，并在不同密度、配置和渗透率的混合交通控制环境中进行评估，并与现有的自动驾驶车辆进行比较。