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:虽然在自动驾驶控制和交通模拟方面有所进展,但探索两者的融合与深层次学习的工作却很少甚至完全没有进展,这两个领域的工作似乎都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语义关系。本文介绍了交通-软件自动驾驶(TrAAD),这是交通信息化学习的通用蒸馏式方法,它直接优化了自主驾驶政策,以全面有利于更快速的交通流量和更低的能源消耗。我们利用了在模仿学习系统中对速度控制进行任意界定的监督,因为大多数驱动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共同模拟的问题,并为未来工作中以自主驾驶方式直接参与交通模拟奠定了基础。我们的结果显示,随着交通模拟信息涉及对仿造学习方法的监督,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。