Fast 3D Object Detection for 4D Imaging Radar integrating Image Map features using Semi-supervised Learning*

Recognition of surrounding traffic participants is important for the safe driving of automated vehicles. Methods using distance measurement sensors from LiDAR and MWR, and image information from cameras have been mainly developed. In recent years, 4D imaging radar has been developed as a next-generation MWR. It can measure three-dimensional position with relative velocity in irradiation direction. In this study, we developed 3D object detection model using 4D imaging radar and evaluated the recognition performance from a practical point of view. The developed model is based on a simple object detection pipeline using point features in BEV. Our model integrates the road structure feature using a predefined image map created using LiDAR without annotation. In addition, we also developed semi-supervised label generation using LiDAR in order to train the model for sparse point clouds. The evaluations show the recognition of vehicles is improved by adding velocity feature and image map information. The results on our dataset show the F-value for the Car class was improved by +18%, and the F-value for the Large Car class was improved by +40% by introducing measured velocity and irradiation direction vector. Then, the precision for the Car class was improved by +16%, and the precision for the Large Car class was improved by +30% by introducing an image map that suppressed false positives with a low processing cost.