用摄像头和激光雷达模拟高精度毫米波雷达信号，解决数据不足问题。
Simulating Automotive Radar with Lidar and Camera Inputs
- 基于相机与激光点云，通过两个神经网络生成4维雷达信号。
- 合成信号在三个商用雷达数据集上验证，质量接近真实数据。
- 增强检测模型性能，适合自动驾驶雷达研究者使用。
低成本毫米波汽车雷达因能在恶劣天气和光照条件下工作而备受关注，但高质量数据集的缺乏制约了研发进展。本文提出一种新方法，利用摄像头图像、激光雷达点云及自车速度，模拟包含俯仰、偏航、距离和多普勒速度的4维毫米波雷达信号，以及信号强度（RSS）。该方法基于两个新神经网络：DIS-Net用于估计雷达信号的空间分布与数量，RSS-Net则根据外观和几何信息预测信号强度。我们在三种不同型号的商用汽车雷达公开数据集上实现并测试了该方法。实验结果表明，所生成的雷达信号具有高保真度。此外，我们使用合成雷达数据增强主流目标检测神经网络的训练，其性能优于仅使用原始雷达数据训练的模型，为未来基于雷达的研究开发提供了有力支持。
原文摘要 · Abstract (English)
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.
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