In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated continuous wave (FMCW) radar. The proposed multi-input deep learning structure is a CNN-based structure using a distance Doppler map and a point cloud map as multiple inputs. The classification accuracy with the range–Doppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps.

中文翻译：

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基于多输入深度学习的FMCW雷达信号分类

在自动驾驶汽车中，紧急制动系统使用激光雷达或雷达传感器识别周围环境并防止事故发生。使用深度学习基于雷达数据的常规分类器是使用距离多普勒图或微多普勒的单输入结构。具有单一输入结构的深度学习在提高分类性能方面存在局限性。在本文中，我们提出了一种基于卷积神经网络（CNN）的多输入分类器，以减少使用调频连续波（FMCW）雷达的计算量并提高分类性能。提出的多输入深度学习结构是基于CNN的结构，使用距离多普勒图和点云图作为多个输入。距离多普勒图或点云图的分类精度分别为85％和92％。两种地图均将其提高到96％。