基于雷达的道路表面分类使用距离快的富里埃变换学习模型
Hyunji Lee1, Jiyun Kim1, Kwangin Ko1
1Convergence Reasearch Center for Disaster & Safety, Advanced Institute of Convergence Technology (AICT), Suwon 16229, Republic of Korea.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
毫米波 (mmWave) 雷达有效地识别危险的道路条件,如冰和雪,优于传统方法. 这项技术通过可靠地检测各种表面状态来提高道路安全,即使在可见度差的情况下也是如此.
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
- 材料科学 材料科学 材料科学
背景情况:
- 黑冰造成的交通事故对公众安全构成重大风险.
- 传统的检测系统在低可见度和恶劣天气中扎.
- 毫米波 (mmWave) 雷达在具有挑战性的条件下提供可靠的道路表面监测.
研究的目的:
- 评估毫米波雷达在识别各种路面条件方面的有效性.
- 为了比较各种机器学习模型在使用毫米波数据对道路状态进行分类时的性能.
主要方法:
- 在青和混凝土上实验模拟了六个路面条件 (干燥,湿,薄冰,冰,雪,污泥).
- 采集的毫米波雷达数据,使用范围-快里叶变换 (Range-Fast Fourier Transform) (范围-FFT) 进行处理.
- 经过训练和评估的分类模型:XGBoost,LightGBM,CNN和ViT.
主要成果:
- 在所有测试的道路条件下,CNN和ViT车型表现出卓越的性能.
- 机器学习模型在危险的道路状态 (冰,雪) 上降低了准确性.
- CNN表现出一致的稳定性,而ViT则提供了具有增强模式识别的竞争性准确性.
结论:
- 毫米波雷达可用于可靠的道路表面状况识别.
- 美国有线电视新闻网 (CNN) 和ViT显示,先进的道路安全系统具有前途.
- 未来的改进可能涉及多式联运传感器融合和时间序列分析.
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