使用优化机器学习算法在自动驾驶汽车之间进行点对点通信的地理空间数据
T M Aruna1, Piyush Kumar2, E Naresh3
1Department of AIML, Nitte Meenakshi Institute of Technology, Bengaluru, India.
Scientific reports
|August 30, 2024
概括
本研究介绍了一种用于自动驾驶汽车网络的新型加权支持向量机 (W-SVM) 内核. 它增强了数据清理和车辆之间的通信,提高了自动驾驶运输系统的安全性和效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 自动驾驶汽车对于未来的交通运输至关重要,但司机错误导致全球超过60%的事故.
- 开发自动驾驶汽车系统在自然灾害识别和车辆间数据交换方面面临挑战.
- 目前人工智能,机器学习,数据科学和大数据方面的进展支持自主系统,但需要专门的网络通信解决方案.
研究的目的:
- 通过专注于数据清理和无交互来解决自动驾驶汽车通信方面的挑战.
- 开发一款针对自动驾驶汽车中的点对点 (P2P) 网络而定制的新型支持矢量机 (SVM) 内核.
- 创建一个优化的混合方法,在SVM中选择重量,以提高性能.
主要方法:
- 提出了一个新的加权支持向量机 (W-SVM) 内核,以满足默瑟定理的约束.
- 开发了一种混合优化策略,将灰狼优化器 (GWO) 和大象群优化 (EHO) 结合起来,用于重量向量导出.
- 实施了数据清理方法,以加强车辆之间的通信.
主要成果:
- 拟议的W-SVM内核通过混合GWO-EHO进行优化,在处理复杂数据问题方面表现出卓越的性能.
- 与单个算法相比,混合优化增强了融合速度,利用和探索能力.
- 这种新的方法在数据清理方面取得了显著的改进,并促进了自动驾驶汽车之间的无互动.
结论:
- 为SVM重量选择开发的混合优化方法对自动驾驶车辆网络有效.
- 新型W-SVM内核为改善自动驾驶数据交换和安全提供了强大的解决方案.
- 这项研究有助于克服广泛采用自动驾驶汽车技术的关键障碍.
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