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

Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities