An Integrative Method of Detection and Localization of Surrounding Vehicles Using GPS and RGB Camera Device Attachments

This study aims to enhance environmental monitoring in a self-driving bus project within an urban environment. Firstly, as previously mentioned, the positions of vehicles in traffic near the self-driving bus should be synchronized. While some researchers utilize LIDAR, RADAR, and camera technologies for environmental monitoring and mapping, these methods have high data requirements and are particularly challenging to implement in real-time operations. This approach will ensure the identification of vehicles using YOLOv9 and the collection of their coordinates. The Pixel Map Match and Filter method will be used to minimize GPS data errors, and camera data will be combined with GPS data based on GPS data interpolation to determine the factors affecting vehicle interaction space. This paper presents the results of this integration, demonstrating improved accuracy and reliability compared to traditional systems, and proposes a technology that can contribute to the development of self-driving vehicles.