Clustering of Closely Adjacent Extended Objects in Radar Images using Velocity Profile Analysis

J. Schlichenmaier, Fabian Roos, Philipp Hügler, C. Waldschmidt
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引用次数: 6

Abstract

As high resolution automotive radars become more common, so does their usage for next-generation functionalities like advanced driver assistant systems and autonomous driving. This creates the need for robust clustering techniques to distinguish among multiple extended objects like vehicles in the same scenario. One especially challenging scenario is that of separating two extended targets close to each other, each following its own trajectory. This paper proposes a clustering algorithm based on the analysis of the velocity profile to divide target points of multiple vehicles into sub-clusters. The theoretical background is explained and shown on simulation data. The algorithm is verified using radar measurements of two extended vehicular targets.
基于速度剖面分析的雷达图像中邻近扩展目标聚类
随着高分辨率汽车雷达变得越来越普遍,它们在高级驾驶辅助系统和自动驾驶等下一代功能中的应用也越来越广泛。这就需要健壮的聚类技术来区分同一场景中的多个扩展对象(如车辆)。一个特别具有挑战性的场景是将两个相互靠近的扩展目标分开,每个目标都遵循自己的轨迹。本文提出了一种基于速度剖面分析的聚类算法,将多辆车的目标点划分为子聚类。通过仿真数据对理论背景进行了说明和说明。通过对两个扩展车辆目标的雷达测量,验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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