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 technique combining 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.

翻译：人类驾驶车辆（HVs）表现出复杂多样的行为。准确建模此类行为对于在仿真中验证机器人车辆（RVs）并实现混合交通控制的潜力至关重要。然而，参数化模型和数据驱动技术等现有方法难以完全捕捉其复杂性及多样性。为解决此问题，本文提出CARL——一种将用于近距离跟车的模仿学习与用于较大车头时距的概率采样相结合的混合技术。同时，我们提出两类基于强化学习的RVs：专注于最大化安全性的安全型RV与专注于最大化通行效率的效率型RV。实验表明，安全型RV将碰撞时间提升至临界4秒阈值以上，并使避免碰撞的减速度降低高达80%；效率型RV则实现通行效率提升最高达49%。这些结果证明了CARL在增强混合交通中安全性与效率方面的有效性。