Deep reinforcement learning-based long-range autonomous valet parking for smart cities
Deep reinforcement learning-based long-range autonomous valet parking for smart cities
复制标题
基于深度强化学习的智慧城市远程自主代客泊车
DOI：
10.1016/j.scs.2022.104311
复制
发表时间：
2023
影响因子：
11.7
通讯作者：
Khalid M
中科院分区：
文献类型：
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作者：
Khalid M
在本文中，为了减少市中心的拥堵率并提高每个用户的出行体验质量（QoE），提出了远程自主代客泊车的框架。在这里，部署一辆自动驾驶汽车（AV）在用户所需的地点接送用户，然后自动驾驶到城市组织良好的地点周围的停车场。在此框架中，我们的目标是最小化 AV 的整体距离，同时保证所有用户都获得良好的 QoE 服务，即通过优化 AV 的路径规划和服务时隙数量，在所需的地点接送用户。为此，我们首先提出一种基于学习的算法，称为双层蚁群优化（DLACO）算法，以迭代的方式解决上述问题。然后，为了做出快速决策，同时考虑动态环境（即自动驾驶汽车可能会从不同位置接送用户），我们进一步提出了一种基于深度强化学习的算法，即深度 Q 学习网络（DQN）来解决这个问题。实验结果表明，DL-ACO 和基于 DQN 的算法均取得了相当可观的性能。
In this paper, to reduce the congestion rate at the city center and increase the travelling quality of experience (QoE) of each user, the framework of long-range autonomous valet parking is presented. Here, an Autonomous Vehicle (AV) is deployed to pick up, and drop off users at their required spots, and then drive to the car park around well-organized places of city autonomously. In this framework, we aim to minimize the overall distance of AV, while guarantee all users are served with great QoE, 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 present a learning-based algorithm, which is named as Double-Layer Ant Colony Optimization (DLACO) algorithm to solve the above problem in an iterative way. Then, to make the fast decision, while considers the dynamic environment (i.e., the AV may pick up and drop off users from different locations), we further present a deep reinforcement learning-based algorithm, i.e., Deep Q-learning Network (DQN) to solve this problem. Experimental results show that the DL-ACO and DQN-based algorithms both achieve the considerable performance.