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 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