Modern Approaches for Improving the Accuracy of Radar-Camera Sensors in Advanced Driver Assistance Systems

This paper explores methods for improving radar-camera sensor accuracy in autonomous driving systems. The choice of sensors is crucial and depends on the specific tasks and environmental conditions. Radar and cameras were selected for their complementary strengths: radar's reliability in all weather conditions and the camera's object classification capabilities. However, camera sensitivity to noises, such as dust and pollution, requires continuous monitoring. To address this, a pollution detection algorithm using morphological operations was developed. While radar excels in object detection under various conditions, its large detection area can generate irrelevant data. A filtering algorithm was implemented to focus radar detections within the relevant field of view. To further enhance localization accuracy, a radar-camera fusion algorithm was created, enabling precise vehicle positioning. Extensive testing with real and simulated systems demonstrated the effectiveness of the proposed methods, highlighting their impact on improving ADAS and autonomous vehicle performance.