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

TL;DR

This paper proposes a novel framework for long-range autonomous valet parking in smart cities, utilizing deep reinforcement learning and ant colony optimization to optimize path planning and service efficiency.

Contribution

It introduces a combined approach using DL-ACO and DQN algorithms for real-time, efficient, and dynamic long-range autonomous parking and user service in urban environments.

Findings

Both algorithms achieve significant performance improvements.

The methods effectively handle dynamic urban environments.

Enhanced path optimization reduces overall travel distance.

Abstract

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.