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：尽管自动驾驶控制和交通模拟领域取得了进展，但几乎没有研究探索它们与深度学习的统一。这两个领域的研究似乎聚焦于完全不同的独立问题，然而在现实世界中，交通和驾驶本质上相互关联。本文提出可区分交通流的自主驾驶（TrAAD）方法，这是一种可泛化的蒸馏式交通感知模仿学习方法，可直接优化交通流速度并降低能耗。TrAAD专注于模仿学习系统中速度控制的监督，因为大多数驾驶研究关注感知和转向。此外，我们的方法解决了交通模拟器和驾驶模拟器之间缺乏协同仿真的问题，为未来将交通模拟直接融入自动驾驶研究奠定了基础。结果表明，通过在模仿学习方法的监督中引入交通模拟信息，自动驾驶车辆能够学会以有利于交通流和附近所有车辆整体能耗的方式加速。