共有自動運転車フリート管理のための深層強化学習
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management
共有自動運転車フリートの空車移動による渋滞や駐車コストを抑えるため、強化学習で乗客待ち時間・駐車費用・空走行を最小化する手法を比較検討した研究。
分類: cs.LG, cs.RO
原文アブストラクト
Shared Automated Vehicles (SAVs) Fleets companies are starting pilot projects nationwide. In 2020 in Fairfax Virginia it was announced the first Shared Autonomous Vehicle Fleet pilot project in Virginia. SAVs promise to improve quality of life. However, SAVs will also induce some negative externalities by generating excessive vehicle miles traveled (VMT), which leads to more congestions, energy consumption, and emissions. The excessive VMT are primarily generated via empty relocation process. Reinforcement Learning based algorithms are being researched as a possible solution to solve some of these problems: most notably minimizing waiting time for riders. But no research using Reinforcement Learning has been made about reducing parking space cost nor reducing empty cruising time. This study explores different \textbf{Reinforcement Learning approaches and then decide the best approach to help minimize the rider waiting time, parking cost, and empty travel