Moon Sunghoon, Kim Younglok
Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.
Sensors (Basel). 2025 Jan 9;25(2):353. doi: 10.3390/s25020353.
In advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance. This paper introduces the Automotive Radar Alignment Detection Network (AutoRAD-Net), a novel model that leverages complex-valued convolutional neural network (CV-CNN) to address azimuth misalignment challenges in automotive radars. By utilizing complex-valued inputs, AutoRAD-Net effectively learns the physical properties of the radar data, enabling precise azimuth alignment. The model was trained and validated using mounting angle offsets ranging from -3° to +3° and exhibited errors no greater than 0.15° across all tested offsets. Moreover, it demonstrated reliable predictions even for unseen offsets, such as -1.7°, showcasing its generalization capability. The predicted offsets can then be used for physical radar alignment or integrated into compensation algorithms to enhance data interpretation accuracy in ADAS applications. This paper presents AutoRAD-Net as a practical solution for azimuth alignment, advancing radar reliability and performance in autonomous driving systems.
在先进驾驶辅助系统（ADAS）中，汽车雷达安装角度的不对准会显著影响目标检测和跟踪的准确性，进而影响系统的安全性和性能。本文介绍了汽车雷达对准检测网络（AutoRAD-Net），这是一种利用复值卷积神经网络（CV-CNN）来解决汽车雷达方位不对准挑战的新型模型。通过使用复值输入，AutoRAD-Net有效地学习雷达数据的物理特性，实现精确的方位对准。该模型使用从-3°到+3°的安装角度偏移进行训练和验证，在所有测试偏移中误差均不超过0.15°。此外，即使对于未见过的偏移量，如-1.7°，它也能做出可靠的预测，展示了其泛化能力。然后，预测的偏移量可用于雷达的物理对准或集成到补偿算法中，以提高ADAS应用中数据解释的准确性。本文将AutoRAD-Net作为方位对准的实用解决方案，提升了自动驾驶系统中雷达的可靠性和性能。
