In this paper, to reduce the congestion rate at the city center and increase the quality of experience (QoE) of each user, the framework of long-range autonomous valet parking (LAVP) is presented, where an Electric Autonomous Vehicle (EAV) is deployed in the city, which can pick up, drop off users at their required spots, and then drive to the car park out of city center autonomously. In this framework, we aim to minimize the overall distance of the EAV, while guarantee all users are served, i.e., picking up, and dropping off users at their required spots through optimizing the path planning of the EAV and number of serving time slots. To this end, we first propose a learning based algorithm, which is named as Double-Layer Ant Colony Optimization (DL-ACO) algorithm to solve the above problem in an iterative way. Then, to make the real-time decision, while consider the dynamic environment (i.e., the EAV may pick up and drop off users from different locations), we further present a deep reinforcement learning (DRL) based algorithm, which is known as deep Q network (DQN). The experimental results show that the DL-ACO and DQN-based algorithms both achieve the considerable performance.

翻译：在本文中,为了减少市中心的拥堵率,提高每个用户的经验质量(QoE),提出了长距离自动泊车(LAVP)框架(LAVP),在这个框架内,在城市部署了电动自动泊车(EAV),该车可以接收、在所需地点让用户下车,然后开车到市中心的停车场。在这个框架内,我们的目标是尽量减少EAV的总体距离,同时保证所有用户都能得到服务,即通过优化EAV的路径规划以及服务时间档的数量,在他们需要的地点接驳和丢弃用户。为此,我们首先提出一种基于学习的算法,称为双拉安托·多洛尼(DL-ACO)算法,以迭接方式解决上述问题。然后,为了做出实时决定,同时考虑动态环境(即EAVA可以从不同地点接收和丢弃用户),我们进一步介绍基于EA的深度强化学习(DRL)和服务时间槽数。为此,我们首先提议一种基于学习的算法,称为双拉-A自动算法(DQ)的实验结果。