论文标题
DeepReflecs：用雷达反射的汽车对象分类的深度学习
DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
论文作者
论文摘要
本文介绍了一种新型的对象类型分类方法，用于使用带有雷达反射的深度学习的汽车应用。该方法提供了对象类信息，例如行人，骑自行车的人，汽车或非出口。通过使用对反射级雷达数据的轻量深度学习方法，该方法既有强大又有效。它填补了手工特征的低表现方法与具有卷积神经网络的高性能方法之间的差距。提出的网络利用了雷达反射数据的特定特征：它处理任意长度的无序列表作为输入，并结合了本地特征和全局特征的提取。在使用实际数据的实验中，提出的网络的表现优于现有的手工或学习功能的方法。一项消融研究分析了所提出的全球环境层的影响。
This paper presents an novel object type classification method for automotive applications which uses deep learning with radar reflections. The method provides object class information such as pedestrian, cyclist, car, or non-obstacle. The method is both powerful and efficient, by using a light-weight deep learning approach on reflection level radar data. It fills the gap between low-performant methods of handcrafted features and high-performant methods with convolutional neural networks. The proposed network exploits the specific characteristics of radar reflection data: It handles unordered lists of arbitrary length as input and it combines both extraction of local and global features. In experiments with real data the proposed network outperforms existing methods of handcrafted or learned features. An ablation study analyzes the impact of the proposed global context layer.
