Jing Mengyuan, Liu Haiqing, Guo Fuyang, Gong Xiaolong
School of Transportation and Logistic Engineering, Shandong Jiaotong University, Jinan 250357, China.
Sensors (Basel). 2025 Apr 27;25(9):2766. doi: 10.3390/s25092766.
Traditional 3D radar can only detect the planar characteristic information of a target. Thus, it cannot describe its spatial geometric characteristics, which is critical for accurate vehicle classification. To overcome these limitations, this paper investigates elevation features using 4D millimeter-wave radar data and presents a maximum likelihood estimation (MLE)-based vehicle classification method. The elevation data collected by 4D radar from a real road scenario are applied for further analysis. By establishing radar coordinate systems and geodetic coordinate systems, the distribution feature of vehicles' elevation is analyzed by spatial geometric transformation referring to the radar installation parameters, and a Gaussian-based probability distribution model is subsequently proposed. Further, the data-driven parameter optimization on likelihood probabilities of different vehicle samples is performed using a large-scale elevation dataset, and an MLE-based vehicle classification method is presented for identifying small and large vehicles. The experimental results show that there are significant differences in elevation distribution from two different vehicle types, where large vehicles exhibit a wider range of left-skewed distribution in different cross-sections, while small vehicles are more concentrated with a right-skewed distribution. The Gaussian-based MLE method achieves an accuracy of 92%, precision of 87% and recall of 98%, demonstrating excellent performance for traffic monitoring and related applications.
传统的3D雷达只能检测目标的平面特征信息。因此，它无法描述其空间几何特征，而这对于准确的车辆分类至关重要。为了克服这些限制，本文利用4D毫米波雷达数据研究仰角特征，并提出了一种基于最大似然估计（MLE）的车辆分类方法。将4D雷达从真实道路场景中采集的仰角数据用于进一步分析。通过建立雷达坐标系和大地坐标系，参照雷达安装参数，通过空间几何变换分析车辆仰角的分布特征，随后提出基于高斯的概率分布模型。此外，利用大规模仰角数据集对不同车辆样本的似然概率进行数据驱动的参数优化，并提出一种基于MLE的车辆分类方法来识别小型和大型车辆。实验结果表明，两种不同车型的仰角分布存在显著差异，大型车辆在不同横截面上呈现出更宽范围的左偏分布，而小型车辆则更集中且呈右偏分布。基于高斯的MLE方法实现了92%的准确率、87%的精确率和98%的召回率，在交通监测及相关应用中表现出优异的性能。
