Kim Junho, Jeong Taewon, Lee Seongwook
School of Electrical and Electronics Engineering, College of ICT Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.
School of Electronics and Information Engineering, College of Engineering, Korea Aerospace University, 76 Hanggongdaehak-ro, Deogyang-gu, Goyang-si 10540, Republic of Korea.
Sensors (Basel). 2023 Jul 17;23(14):6472. doi: 10.3390/s23146472.
The reliability and safety of advanced driver assistance systems and autonomous vehicles are highly dependent on the accuracy of automotive sensors such as radar, lidar, and camera. However, these sensors can be misaligned compared to the initial installation state due to external shocks, and it can cause deterioration of their performance. In the case of the radar sensor, when the mounting angle is distorted and the sensor tilt toward the ground or sky, the sensing performance deteriorates significantly. Therefore, to guarantee stable detection performance of the sensors and driver safety, a method for determining the misalignment of these sensors is required. In this paper, we propose a method for estimating the vertical tilt angle of the radar sensor using a deep neural network (DNN) classifier. Using the proposed method, the mounting state of the radar can be easily estimated without physically removing the bumper. First, to identify the characteristics of the received signal according to the radar misalignment states, radar data are obtained at various tilt angles and distances. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input. The proposed angle estimator determines the tilt angle of the radar sensor regardless of the measured distance. The average estimation accuracy of the proposed DNN-based classifier is over 99.08%. Therefore, through the proposed method of indirectly determining the radar misalignment, maintenance of the vehicle radar sensor can be easily performed.
先进驾驶辅助系统和自动驾驶车辆的可靠性与安全性高度依赖于雷达、激光雷达和摄像头等汽车传感器的准确性。然而，由于外部冲击，这些传感器与初始安装状态相比可能会出现校准偏差，这会导致其性能下降。对于雷达传感器而言，当安装角度发生扭曲且传感器向地面或天空倾斜时，传感性能会显著恶化。因此，为了确保传感器的稳定检测性能和驾驶员安全，需要一种确定这些传感器校准偏差的方法。在本文中，我们提出了一种使用深度神经网络（DNN）分类器来估计雷达传感器垂直倾斜角度的方法。使用所提出的方法，无需物理拆除保险杠即可轻松估计雷达的安装状态。首先，为了识别根据雷达校准偏差状态接收到的信号的特征，在各种倾斜角度和距离下获取雷达数据。然后，我们从接收到的信号中提取距离剖面图，并使用这些剖面图作为输入设计基于DNN的估计器。所提出的角度估计器可确定雷达传感器的倾斜角度，而与测量距离无关。所提出的基于DNN的分类器的平均估计准确率超过99.08%。因此，通过所提出的间接确定雷达校准偏差的方法，可以轻松地对车辆雷达传感器进行维护。
