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.

翻译：自主泊车（AP）是一种无需人工干预即可将智能车辆导航至停车位的新兴技术。现有的基于数学优化或机器学习的AP方法可能因执行时间过长或泛化能力不足而导致潜在失效。为弥补这一缺陷，本文提出了一种融合约束优化与模仿学习（iCOIL）方法，以实现高效可靠的AP。iCOIL方法具备两种候选工作模式（CO和IL），并采用混合场景分析（HSA）模型来判定不同场景下的最优模式。我们在澳门赛车元宇宙（MoCAM）平台上对iCOIL进行了实现与验证。结果表明，iCOIL能在整个AP过程中恰当适应不同场景，并取得显著高于其他基准方法的成功率。