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.
@article{arxiv.2305.13663,
title = {iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning},
author = {Lexiong Huang and Ruihua Han and Guoliang Li and He Li and Shuai Wang and Yang Wang and Chengzhong Xu},
journal= {arXiv preprint arXiv:2305.13663},
year = {2023}
}