在5G中使用卷积神经网络进行V2V通信的移交
Sarah M Alhammad1, Doaa Sami Khafaga1, Mahmoud M Elsayed2
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Heliyon
|August 22, 2024
概括
本研究使用深度学习 (DL) 与5G网络进行车辆检测和障碍物识别,达到97%的准确性. 一种新的移交预测方法可以提高异质网络的性能.
科学领域:
- 电信工程 电信工程 电信工程
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 车辆通信对于交通流动和安全至关重要.
- 5G技术为先进的运输系统提供了高数据速率和服务质量.
- 深度学习 (DL) 在处理大型数据集以识别特征方面表现出色.
研究的目的:
- 在5G环境中使用DL检测车辆和识别障碍物.
- 为异质网络开发一种新的水平移交预测方法.
- 提高车辆通信效率和安全.
主要方法:
- 使用VGG19深度学习模型通过转移学习来检测车辆和障碍物.
- 基于频道特征提出了一个新的水平交付预测算法.
- 在模拟的5G网络环境中实施和评估算法.
主要成果:
- 在识别车辆方面取得了97%的成功率.
- 成功预测了下一个水平交付站.
- 证明了DL算法在5G车辆通信中的有效性.
结论:
- 提出的基于DL的车辆检测和交付预测方法在5G环境中是有效的.
- VGG19模型和新的交付算法显示出高精度和可靠性.
- 这项研究有助于更安全,更有效的智能运输系统.
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