探索基于单头和多头CNN和LSTM的模型,用于使用车载车辆多IMU数据进行路面分类
Luis A Arce-Saenz1, Javier Izquierdo-Reyes2, Rogelio Bustamante-Bello1
1School of Engineering and Science, Tecnologico de Monterrey, Mexico City, 14380, Mexico.
Scientific reports
|July 9, 2025
概括
使用惯性测量单元 (IMU) 数据的深度学习模型有效地对路面状况进行分类. 将卷积神经网络 (CNN) 与长期短期记忆 (LSTM) 层相结合,实现了道路监控安全的最高精度.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 运输安全运输安全
背景情况:
- 道路表面监测对于车辆和行人安全至关重要.
- 强大的数据采集和分析是准确评估道路状况的关键.
研究的目的:
- 使用深度学习架构对路面状况进行分类.
- 评估卷积神经网络 (CNN) 和CNN与长短期记忆 (LSTM) 层相结合.
- 分析车辆弹和未弹质量的惯性测量单位 (IMU) 的数据.
主要方法:
- 实施单头和多头深度学习模型 (CNN,CNN+LSTM).
- 利用来自IMU在各种车辆位置的加速和角速度数据.
- 通过网格搜索进行超参数调整,以获得最佳模型配置.
主要成果:
- 在性能方面,CNN+LSTM模型通常超过了CNN-only模型.
- 最顶级的模型,一个带有三个IMU的单头架构,实现了0.9338.8的宏F1得分.
- 在单头架构中结合IMU数据的有效性得到了证明.
结论:
- 深度学习,特别是CNN+LSTM,显示了道路表面状况分类的重大前景.
- 优化模型架构和扩展数据集可以进一步提高分类准确性.
- 这种方法通过先进的监测提供了一条改善道路安全的途径.
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