Radar Signal Abnormal Point Classification based on Camera-Radar Sensor Fusion

For safe driving, it is essential to accept reliable information from recognition sensors. In this paper, we present a deep learning model that classifies whether radar signals coming in are normal or abnormal. The abnormal signal is defined as noise from the radar and all signals received when the radar fails or is in trouble. It is difficult to determine whether reflected signals are normal or not based only on radar data. Therefore, the camera and radar sensors are used together, considering the radar cross section (RCS) distribution varies by the angle and distance of the object. The proposed model uses data received from camera and radar sensors to determine the normality of object signals. The model shows an accuracy of 96.24%. Through the results of this study, the reliability of radar signals can be determined in the actual driving environment, thereby ensuring the safety of vehicles and pedestrians.