基于雷达传感器的机器学习方法用于精确估计车辆位置
Muhammad Sohail1, Abd Ullah Khan2, Moid Sandhu3
1Riphah College of Computing, Riphah International University Faisalabad, Faisalabad, Pakistan.
Scientific reports
|August 24, 2023
概括
本研究引入了一种基于雷达的新方法,用于在车辆定时网络 (VANETs) 中精确定位车辆. 该技术通过使用动态雷达数据来提高准确性,并在实时交通场景中优于现有方法.
科学领域:
- * 车辆定时网络 (VANETs) 的使用
- * 传感器融合技术
- * 雷达信号处理处理
背景情况:
- * 传统的全球定位系统 (GPS) 和全球导航卫星系统 (GNSS) 方法存在数据延迟和错误,限制了动态环境中的精确车辆定位.
- * 现有的基于雷达的方法使用静态距离和 азимут,在高度动态的交通条件下被证明是低效的.
- * 准确的相对车辆定位对于连接车辆的安全,自主和可靠运行至关重要.
研究的目的:
- * 提出基于雷达的相对车辆定位估计方法,以提高VANET的精度.
- * 根据参考车辆的速度,利用频率调制连续波 (FMCW) 雷达的动态范围和光径.
- *通过精确的实时定位来提高自动驾驶系统的安全性和可靠性.
主要方法:
- * 开发了一种基于雷达的相对定位技术,利用FMCW雷达传感器的动态范围和光度.
- * 包含参考车辆的速度,以动态调整距离和向测量.
- *采用了You Only Look Once (YOLO) 版本4算法,从雷达数据中计算精度和IOU上的交叉点.
- *专注于附近的车辆,对相对距离和速度的估计有很高的相互作用概率.
主要成果:
- * 在车辆位置估计中获得了80.0%的精度.
- * 获得了超过欧盟 (IOU) 的交叉点值高达87.14%.
- * 在各种实时流量场景下,与最先进的方法相比,表现出优越的性能.
结论:
- * 拟议的基于雷达的方法显著提高了动态环境中的相对车辆定位的精度.
- *动态利用雷达参数和YOLOv4集成为自动驾驶车辆导航提供了强大的解决方案.
- *这种方法为VANET应用程序提供了传统定位系统的可靠替代方案.
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