提高机场流量:基于VLC,重定向技术和自适应奖励学习的智能系统
Manuela Vieira1,2,3, Manuel Augusto Vieira1,2, Gonçalo Galvão1,3
1Electronics Telecommunication and Computer Department, Instituto Superior de Engenharia de Lisboa-Instituto Politécnico de Lisboa, 1949-014 Lisboa, Portugal.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种使用可见光通信 (VLC) 和深度增强学习 (DRL) 来优化导航的AI驱动的机场交通系统. 该系统通过智能管理行人和自动驾驶车辆 (AGV) 交通流量来提高安全性和效率.
科学领域:
- 智能运输系统 智能运输系统
- 在移动方面的人工智能.
- 无线通信网络 无线通信网络
背景情况:
- 机场需要有效地定位和管理行人和自动驾驶汽车 (AGV) 的交通.
- 现有系统在优化复杂的机场流量和确保安全方面面临挑战.
- 无需GPS的无室内导航对于运营效率至关重要.
研究的目的:
- 开发一个人工智能驱动的机场交通管理系统,集成可见光通信 (VLC) 和深度强化学习 (DRL).
- 为了优化交通流动,减少拥堵,并提高行人和农用车的安全性.
- 为了实现准确的室内定位,并提高整体机场流动性.
主要方法:
- 使用带开关调制的四色LED和VLC的SiC光学接收器实现混合网状网络.
- 开发使用深度强化学习 (DRL) 和Q学习算法进行交通分析和决策的AI代理.
- 整合重定向技术和适应性奖励机制,以实现动态交通负载平衡和避免瓶.
主要成果:
- 实现了更平衡的绿色时间分配,减少了高达43%的车辆优先级阶段,以适应行人流动.
- 改进了路线规划,减少了停车时间,加强了 AGV 和行人交通之间的协调.
- 确认保留了交通流动的响应能力和稳定的关键清除阶段,尽管行人优先.
结论:
- 拟议的人工智能驱动的VLC系统通过优化行人和AGV协调,显著提高了机场交通管理.
- 整合DRL和适应性策略导致更安全,更有效,更以人为中心的机场流动.
- 该系统提供了准确的室内定位,支持无操作,无需依赖GPS.
相关概念视频
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01:10Control Systems
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01:28Load-frequency control
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