交通流を考慮した微分可能交通シミュレーションによる自動運転
Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation
交通シミュレーションを微分可能な形で自動運転の模倣学習に統合し、周囲の交通流を改善しつつエネルギー消費を抑える速度制御を学習する手法を提案。
著者: Laura Zheng, Sanghyun Son, Ming C. Lin
分類: cs.RO, cs.MA
原文アブストラクト
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.
