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.

翻译：人类驾驶车辆（HVs）表现出复杂多样的行为。精确建模此类行为对于在仿真中验证机器人车辆（RVs）以及实现混合交通控制的潜力至关重要。然而，现有方法（如参数化模型和数据驱动技术）难以全面捕捉其复杂性与多样性。为此，本研究提出CARL——一种混合方法，它结合了近距离跟驰的模仿学习与大车头距的概率采样。我们还提出了两类基于强化学习的RVs：专注于最大化安全性的安全型RV，以及专注于最大化通行效率的效率型RV。实验表明，安全型RV能将碰撞时间提升至超过4秒临界阈值，并将避免碰撞的减速率降低达80%；而效率型RV可实现高达49%的通行量提升。这些结果证明了CARL在提升混合交通安全性与效率方面的有效性。