iCOIL: 制約付き最適化と模倣学習を統合したシナリオ適応型自動駐車
iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning
自動駐車において、制約付き最適化と模倣学習をシナリオに応じて切り替える統合手法を提案し、メタバース環境で高い成功率を達成した。
著者: Lexiong Huang, Ruihua Han, Guoliang Li, He Li, Shuai Wang, Yang Wang, Chengzhong Xu
分類: cs.RO
原文アブストラクト
Autonomous parking (AP) is an emering technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.
