This article presents an integrated sensor fusion (ISF) solution based on the multiple-input, multiple-output (MIMO) radar, camera, and on-device computing. The MIMO radar is capable of estimating an object’s attributes in four dimensions—range, velocity, azimuth angle, and elevation angle—which can be further used to estimate the length, width, and height of the object. The camera is responsible for object classification based on deep learning. The respective signal processing pipelines and the fusion of results are carried by the on-device computing platform. These two sensors complement each other very well in detecting and classifying traffic objects. Compared with existing sensor fusion solutions based on multiple distributed devices, ISF exhibits superior performance in terms of latency and the total cost of ownership (TCO). It also simplifies time synchronization among different sensors and facilitates the deeper fusion of the signal processing algorithms of different sensors. The comprehensive roadside sensing capabilities provided by the ISF solution can enhance the safety and efficiency of both the automated driving and human driving of connected vehicles.

中文翻译：

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基于 4D MIMO 雷达和摄像头的集成传感器融合：车联网应用解决方案


本文提出了一种基于多输入多输出 (MIMO) 雷达、摄像头和设备内计算的集成传感器融合 (ISF) 解决方案。 MIMO雷达能够估计物体的距离、速度、方位角和仰角四个维度的属性，进而可以估计物体的长度、宽度和高度。相机负责基于深度学习的物体分类。各自的信号处理管道和结果的融合由设备上的计算平台承载。这两个传感器在检测和分类交通物体方面可以很好地互补。与现有基于多个分布式设备的传感器融合解决方案相比，ISF在延迟和总拥有成本（TCO）方面表现出优越的性能。它还简化了不同传感器之间的时间同步，有利于不同传感器信号处理算法的更深层次融合。 ISF解决方案提供的全面路边传感能力可以提高联网车辆自动驾驶和人类驾驶的安全性和效率。