The integration of self-driving technologies within Cooperative Intelligent Transportation Systems (C-ITS) presents a promising avenue for revolutionizing urban mobility. This project delves into the critical aspect of data fusion, focusing on merging information from diverse sensors and vehicles to improve the performance and reliability of autonomous vehicles. Leveraging the concept of Smart Mobility, our project explores the use of Local Dynamic Map (LDM) data to address the challenges related to this task. At the core of our research the information to be merged could come from disparate sources, including Lidar, radar, cameras, and vehicle-to-vehicle communication, to provide a comprehensive and accurate representation of the surrounding environment.