研究基于车辆振动数据的道路表面识别算法
Jianfeng Cui1, Hengxu Zhang1, Xiao Wang2
1State Key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究引入了一种改进的1D-CNN算法,用于连接和自动化车辆 (CAV) 的道路表面识别. 这种新的方法实现了高精度,提高了驾驶安全,降低了维护成本.
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
- 人工智能的人工智能
背景情况:
- 道路表面状况极大地影响驾驶安全和车辆维护成本.
- 准确的路面识别对于连接和自动化车辆 (CAV) 的环境感知至关重要.
- 传统的方法在有效的特征提取方面扎,以确定道路表面.
研究的目的:
- 开发一个改进的1D-CNN算法,用于增强道路表面识别.
- 为了降低计算成本和培训时间,同时保持高精度.
- 提高通用化能力,并抑制道路表面识别模型中的过度拟合.
主要方法:
- 开发了一种振动信号采集系统,用于高质量的数据收集.
- 提出了一个基于VGG16架构的优化1D-CNN算法.
- 集成数据增强,Adam优化和L2规范化技术.
主要成果:
- 优化的1D-CNN模型具有较少的参数数量 (101.6k),降低了计算需求.
- 在公开数据集上达到99.3%的高识别准确率,在实际车辆测试中达到99.4%.
- 在不同的数据源中表现出强大的适应性,优于传统方法.
结论:
- 拟议的1D-CNN算法在准确和高效的道路表面识别方面取得了重大进展.
- 这项技术对提高联网和自动化汽车的安全性和效率具有实际意义.
- 该方法为自动驾驶系统的环境感知挑战提供了强大的解决方案.
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