Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning

Javier Martínez García, Robert Prophet, Juan Carlos Fuentes Michel, R. Ebelt, M. Vossiek, Ingo Weber
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引用次数: 15

Abstract

We introduce a method to classify ghost moving detections in automotive radar sensors for advanced driver assistance systems. A fully connected network is used to distinguish between real and false moving detections in the occupancy gridmaps. By using this architecture, we combine the local Doppler information, along with the spatial context of the surrounding scenario to classify the moving detections. A proof of concept experiment shows promising results with data from a test drive in an urban scenario.
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基于深度学习的汽车场景幽灵运动检测识别
介绍了一种用于高级驾驶辅助系统的汽车雷达传感器中鬼魂运动检测的分类方法。利用全连通网络来区分占用网格图中运动检测的真假。通过使用该架构,我们结合局部多普勒信息以及周围场景的空间背景来对运动检测进行分类。一项概念验证实验显示,在城市场景中进行的试驾数据显示出了令人鼓舞的结果。
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