  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%.
