While there have been advancements in autonomous driving control and traffic simulation, there have been little to no works exploring their unification with deep learning. Works in both areas seem to focus on entirely different exclusive problems, yet traffic and driving are inherently related in the real world. In this paper, we present Traffic-Aware Autonomous Driving (TrAAD), a generalizable distillation-style method for traffic-informed imitation learning that directly optimizes for faster traffic flow and lower energy consumption. TrAAD focuses on the supervision of speed control in imitation learning systems, as most driving research focuses on perception and steering. Moreover, our method addresses the lack of co-simulation between traffic and driving simulators and provides a basis for directly involving traffic simulation with autonomous driving in future work. Our results show that, with information from traffic simulation involved in the supervision of imitation learning methods, an autonomous vehicle can learn how to accelerate in a fashion that is beneficial for traffic flow and overall energy consumption for all nearby vehicles.

翻译：尽管自动驾驶控制与交通仿真领域取得了进展，但鲜有研究探索其与深度学习的统一。两类领域的研究似乎专注于截然不同的特定问题，然而在真实世界中，交通与驾驶本质上是相互关联的。本文提出交通感知自动驾驶（TrAAD）——一种可泛化的蒸馏式方法，用于实现交通信息引导的模仿学习，可直接优化交通流速度并降低能耗。由于现有驾驶研究主要聚焦于感知与转向控制，TrAAD方法专注于模仿学习系统中速度控制的监督。此外，本方法弥补了交通仿真与驾驶仿真之间缺乏协同仿真的不足，为未来将交通仿真直接融入自动驾驶研究提供了基础。实验结果表明，通过在模仿学习方法的监督中引入交通仿真信息，自动驾驶车辆能够学习到有利于提升交通流效率并降低周围所有车辆整体能耗的加速策略。