  Human-driven vehicles (HVs) exhibit complex and diverse behaviors. Accurately
modeling such behavior is crucial for validating Robot Vehicles (RVs) in
simulation and realizing the potential of mixed traffic control. However,
existing approaches like parameterized models and data-driven techniques
struggle to capture the full complexity and diversity. To address this, in this
work, we introduce CARL, a hybrid approach that combines imitation learning for
close proximity car-following and probabilistic sampling for larger headways.
We also propose two classes of RL-based RVs: a safety RV focused on maximizing
safety and an efficiency RV focused on maximizing efficiency. Our experiments
show that the safety RV increases Time-to-Collision above the critical 4-second
threshold and reduces Deceleration Rate to Avoid a Crash by up to 80%, while
the efficiency RV achieves improvements in throughput of up to 49%. These
results demonstrate the effectiveness of CARL in enhancing both safety and
efficiency in mixed traffic.
