Human-driven vehicles (HVs) amplify naturally occurring perturbations in traffic, leading to congestion--a major contributor to increased fuel consumption, higher collision risks, and reduced road capacity utilization. While previous research demonstrates that Robot Vehicles (RVs) can be leveraged to mitigate these issues, most such studies rely on simulations with simplistic models of human car-following behaviors. In this work, we analyze real-world driving trajectories and extract a wide range of acceleration profiles. We then incorporates these profiles into simulations for training RVs to mitigate congestion. We evaluate the safety, efficiency, and stability of mixed traffic via comprehensive experiments conducted in two mixed traffic environments (Ring and Bottleneck) at various traffic densities, configurations, and RV penetration rates. The results show that under real-world perturbations, prior RV controllers experience performance degradation on all three objectives (sometimes even lower than 100% HVs). To address this, we introduce a reinforcement learning based RV that employs a congestion stage classifier to optimize the safety, efficiency, and stability of mixed traffic. Our RVs demonstrate significant improvements: safety by up to 66%, efficiency by up to 54%, and stability by up to 97%.

翻译：人类驾驶车辆会放大交通中自然产生的扰动，进而引发拥堵——这成为导致燃油消耗增加、碰撞风险升高及道路容量利用率下降的主要因素。尽管已有研究表明机器人车辆可被用于缓解这些问题，但多数研究依赖采用简化的人类跟车行为模型的仿真。本研究通过分析真实驾驶轨迹，提取了多种加速度分布特征，并将其引入仿真环境以训练机器人车辆缓解拥堵。我们在环形和瓶颈两种混合交通场景下，针对不同交通密度、配置及机器人车辆渗透率开展了综合实验，评估混合交通的安全性、效率及稳定性。结果表明，在真实世界扰动下，现有机器人车辆控制器的三项性能指标均出现退化（甚至低于100%人类驾驶车辆）。为解决这一问题，我们提出了一种基于强化学习的机器人车辆，该车辆采用拥塞阶段分类器以优化混合交通的安全性、效率与稳定性。我们的机器人车辆实现了显著提升：安全性提升高达66%，效率提升高达54%，稳定性提升高达97%。