Determining Misalignment State of Automotive Radar Sensor Using DNN

An automotive radar sensor can be misaligned compared to the initial installation state due to various external shocks while driving, and it can cause deterioration of radar detection performance. To guarantee the stable detection performance of the radar, a method of estimating the deviation angle compared to the initial state is required. To directly check the radar misalignment, an inefficient bumper removal process is required, so a method of indirectly determining the mounting state of the radar is required. Therefore, in this paper, we propose a method for estimating the tilt angle of the radar sensor using deep neural networks (DNNs). First, radar sensor data are obtained at various radar tilt angles and measurement distances to identify the characteristics of received signals. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input vectors. The proposed angle estimator consists of several DNNs in parallel, and the input vector passes through one of them according to the distance estimated from the range profile. Finally, the DNN determines the tilt angle for the input vector. In our datasets, the average classification accuracy of the proposed DNN-based classifier is over 98%.