Low-cost millimeter automotive radar has received more and more attention due to its ability to handle adverse weather and lighting conditions in autonomous driving. However, the lack of quality datasets hinders research and development. We report a new method that is able to simulate 4D millimeter wave radar signals including pitch, yaw, range, and Doppler velocity along with radar signal strength (RSS) using camera image, light detection and ranging (lidar) point cloud, and ego-velocity. The method is based on two new neural networks: 1) DIS-Net, which estimates the spatial distribution and number of radar signals, and 2) RSS-Net, which predicts the RSS of the signal based on appearance and geometric information. We have implemented and tested our method using open datasets from 3 different models of commercial automotive radar. The experimental results show that our method can successfully generate high-fidelity radar signals. Moreover, we have trained a popular object detection neural network with data augmented by our synthesized radar. The network outperforms the counterpart trained only on raw radar data, a promising result to facilitate future radar-based research and development.

翻译：低成本毫米波汽车雷达因其在自动驾驶中应对恶劣天气与光照条件的能力而受到越来越多的关注。然而，高质量数据集的缺乏阻碍了相关研究与发展。本文提出一种新方法，能够利用相机图像、激光雷达点云及自车速度，模拟包含俯仰角、偏航角、距离、多普勒速度以及雷达信号强度的四维毫米波雷达信号。该方法基于两个新的神经网络：1) DIS-Net，用于估计雷达信号的空间分布与数量；2) RSS-Net，基于外观与几何信息预测信号强度。我们使用来自三种商用汽车雷达型号的公开数据集对该方法进行了实现与测试。实验结果表明，本方法能够成功生成高保真雷达信号。此外，我们使用经合成雷达数据增强的数据训练了一个流行的目标检测神经网络。该网络的性能优于仅使用原始雷达数据训练的对照模型，这一结果为推动未来基于雷达的研究与发展提供了有前景的途径。