对物联网边缘的增强CNN基于的方法使智能汽车驾驶系统能够改善实时控制和导航
Majjari Sudhakar1, K Vivekrabinson2
1Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, Tamil Nadu, India. majjarisudhakar@gmail.com.
Scientific reports
|October 1, 2025
概括
这项研究利用物联网边缘计算和卷积神经网络 (CNN) 增强了自动驾驶汽车的控制能力. 该系统提高了导航准确度,减少了计算延迟,从而实现更智能的驾驶.
科学领域:
- 智能运输系统 智能运输系统
- 边缘计算 边缘计算
- 自动驾驶自动驾驶的自动驾驶
背景情况:
- 人驾驶汽车与IoT边缘启用智能驾驶系统有很大的不同.
- 实时控制方向盘,油门和车对于自主导航至关重要.
- 边缘计算为自主系统中的实时数据处理提供了潜力.
研究的目的:
- 调查关键的控制因素,使人驾驶汽车与IoT边缘启用智能驾驶系统有所区别.
- 通过边缘计算和高精度传感器改进自动驾驶汽车的决策.
- 开发和评估基于卷积神经网络 (CNN) 的系统,用于增强车辆导航.
主要方法:
- 从LiDAR,雷达,GPS,IMU和超声波传感器收集的实时时间序列数据.
- 在云服务器传输之前,利用边缘节点预处理传感器数据.
- 采用CNN架构,具有卷积,聚合和完全链接的层,用于控制向量预测,并通过图像数据增强.
主要成果:
- 与传统方法相比,基于CNN的边缘启用驾驶系统显示出更高的导航准确性.
- 实现了 15.123 的根平均平方误差 (RMSE) 和 2.114.14 的损失值.
- 边缘计算有效地减少了计算延迟,提高了车辆的自主性.
结论:
- 边缘计算显著提高了车辆的自主性,并减少了智能驾驶系统中的处理延迟.
- 拟议的基于CNN的方法为自主导航提供了更有效的解决方案.
- 未来的工作将涉及现实世界的验证,以评估系统在动态环境中的性能.
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