  Connected and Automated Vehicles (CAVs) offer a promising solution to the
challenges of mixed traffic with both CAVs and Human-Driven Vehicles (HDVs). A
significant hurdle in such scenarios is traffic oscillation, or the
"stop-and-go" pattern, during car-following situations. While HDVs rely on
limited information, CAVs can leverage data from other CAVs for better
decision-making. This allows CAVs to anticipate and mitigate the spread of
deceleration waves that worsen traffic flow. We propose a novel "CAV-AHDV-CAV"
car-following framework that treats the sequence of HDVs between two CAVs as a
single entity, eliminating noise from individual driver behaviors. This deep
reinforcement learning approach analyzes vehicle equilibrium states and employs
a state fusion strategy. Trained and tested on diverse datasets (HighD, NGSIM,
SPMD, Waymo, Lyft) encompassing over 70,000 car-following instances, our model
outperforms baselines in collision avoidance, maintaining equilibrium with both
preceding and leading vehicles and achieving the lowest standard deviation of
time headway. These results demonstrate the effectiveness of our approach in
developing robust CAV control strategies for mixed traffic. Our model has the
potential to mitigate traffic oscillation, improve traffic flow efficiency, and
enhance overall safety.
