Ground Reflection-Based Misalignment Detection of Automotive Radar Sensors

In this paper, we propose a method for detecting the misalignment of automotive radar sensors. Ensuring the accurate operation of automotive radar sensors is essential for the safety of drivers and passengers. However, when radar sensors are misaligned, they may perceive the surrounding road environment inaccurately, providing incorrect information to the vehicle’s control systems. This can lead to erroneous decisions and potentially cause traffic accidents. Therefore, a method of examining the alignment state of the automotive radar sensors is required. Furthermore, due to the time-consuming and expensive nature of removing the bumper to access the radar sensor for direct inspection, an alternative method of detecting misalignment indirectly is necessary. Our method enables the detection of misalignment in automotive radar sensors through the utilization of ground reflections of radar signals. Depending on the mounting angle of the radar sensor, the range-Doppler (RD) map generated from the received signal reflected from the ground varies significantly. This RD map can be used to classify the mounting angle of the radar sensor to detect the misalignment. We design a convolutional neural network (CNN)-based classifier with the RD map as input. Using the CNN-based classifier, we estimated the mounting angle of the radar sensor with an average accuracy of 94.72%, demonstrating that our proposed method can detect the misalignment of the radar sensor effectively.