While there have been advancements in autonomous driving control and traffic simulation, there have been little to no works exploring the unification of both with deep learning. Works in both areas seem to focus on entirely different exclusive problems, yet traffic and driving have inherent semantic relations 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 a autonomous driving policy for the overall benefit of faster traffic flow and lower energy consumption. We capitalize on improving the arbitrarily defined supervision of speed control in imitation learning systems, as most driving research focus on perception and steering. Moreover, our method addresses the lack of co-simulation between traffic and driving simulators and lays groundwork for directly involving traffic simulation with autonomous driving in future work. Our results show that, with information from traffic simulation involved in 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模拟领域已取得进展，但鲜有研究探索如何将两者与深度学习统一。这两个领域的研究似乎聚焦于完全不同的孤立问题，然而在实际世界中，交通与驾驶之间存在内在的语义关联。本文提出交通感知自主驾驶（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，该方法直接优化自主驾驶策略，以提升整体交通流速度并降低能耗。我们致力于改进模仿学习系统中速度控制的任意定义监督方式，因为多数驾驶研究侧重于感知与转向。此外，我们的方法解决了交通模拟与驾驶模拟器之间缺乏联合仿真的问题，为未来将交通模拟直接融入自主驾驶研究奠定基础。结果表明，通过将交通模拟信息纳入模仿学习方法的监督过程，自主车辆能够学习以有利于交通流及周围所有车辆整体能耗的方式加速行驶。