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：虽然在自主驾驶控制和交通模拟方面取得了进步,但是在探索与深层学习的统一方面几乎没有什么进展,在这两个领域的工作似乎侧重于完全不同的独家问题,但交通和驾驶在现实世界中有着内在的联系。在本文中,我们介绍了交通-汽车自动驾驶(TrAAD),这是交通信息化仿造学习的通用蒸馏式方法,它直接优化了交通流量的加快和能源消耗的降低。TrAAD侧重于对模仿学习系统的速度控制的监督,因为大多数驾驶研究都侧重于感知和方向。此外,我们的方法解决了交通和驾驶模拟器之间缺乏联合模拟的问题,并为将交通模拟与自主驾驶直接纳入未来工作提供了一个基础。我们的结果显示,在对仿造方法进行监督的交通模拟信息中,自主车辆可以学习如何加快速度,从而有利于交通流动和附近所有车辆的总体能源消耗。</s>