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：尽管自主驾驶控制与交通仿真领域已取得进展，但极少有研究探索将二者与深度学习相结合。两个领域的研究看似聚焦于截然不同的问题，然而在现实世界中，交通与驾驶本质上是相互关联的。本文提出交通感知自主驾驶方法(TrAAD)，这是一种可泛化的蒸馏式方法，用于实现交通感知的模仿学习，能够直接优化交通流速度与能耗。TrAAD专注于模仿学习系统中速度控制的监督——因为多数驾驶研究集中于感知与转向。此外，本方法解决了交通仿真与驾驶仿真之间缺乏联合仿真的问题，并为未来将交通仿真直接融入自主驾驶的研究奠定了基础。实验结果表明，通过在模仿学习的监督过程中引入交通仿真信息，自主车辆能够以有利于提升整体交通流效率并降低所有邻近车辆能耗的方式学习加速策略。