Currently, various sensors have been used for advanced driver assistance systems. In order to overcome the limitations of individual sensors, sensor fusion has recently attracted the attention in the field of intelligence vehicles. Thus, vision and radar based sensor fusion has become a popular concept. The typical method of sensor fusion involves vision sensor that recognizes targets based on ROIs (Regions Of Interest) generated by radar sensors. Especially, because AVM (Around View Monitor) cameras due to their wide-angle lenses have limitations of detection performance over near distance and around the edges of the angle of view, for high performance of sensor fusion using AVM cameras and radar sensors the exact ROI extraction of the radar sensor is very important. In order to resolve this problem, we proposed a sensor fusion scheme based on commercial radar modules of the vendor Delphi. First, we configured multiple radar data logging systems together with AVM cameras. We also designed radar post-processing algorithms to extract the exact ROIs. Finally, using the developed hardware and software platforms, we verified the post-data processing algorithm under indoor and outdoor environments.
Research Interests
Radar Sensor; 레이더 센서; Radar System; 레이더 시스템; Radar Signal Processing; 레이더; Radar Detection; 레이더 탐지; Radar Classification; 레이더 인지; Automotive Radar; 차량용 레이더; Surveillance Radar; 감시 레이더; Commercial Radar; 산업 레이더; Defence Radar; 국방 레이더; IoT Radar; IoT 레이더