  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.
