Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2210.03772
