深層強化学習による視覚ベースのエンドツーエンド適応型クルーズコントロール
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning
Unityの高速道路シミュレーション上でDouble Deep Q-networkを用い、カメラ映像から直接車間距離とスロットル/ブレーキを制御するACCシステムを構築し、ICE車とEVで報酬関数とパワートレイン特性の関係を評価した。
著者: Zhensong Wei, Yu Jiang, Xishun Liao, Xuewei Qi, Ziran Wang, Guoyuan Wu, Peng Hao, Matthew Barth
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system. A simulation environment of a highway scene was set up in Unity, which is a game engine that provided both physical models of vehicles and feature data for training and testing. Well-designed reward functions associated with the following distance and throttle/brake force were implemented in the reinforcement learning model for both internal combustion engine (ICE) vehicles and electric vehicles (EV) to perform adaptive cruise control. The gap statistics and total energy consumption are evaluated for different vehicle types to explore the relationship between reward functions and powertrain characteristics. Compared with the traditional radar-based ACC systems or human-in-the-loop simulation, the proposed vision-based ACC system can generate either a better gap regulated trajectory or a smoother speed trajectory depending on the preset reward function. The proposed system can be well adaptive to different speed trajectories of the preceding vehicle and operated in real-time.
