  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 Autonomous Vehicle (AV)
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 AV, 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 AV 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 AV 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.
