Autonomous vehicles currently rely on High-Definition (HD) maps for precise localization and path planning. However, traditional HD mapping approaches suffer from high costs, inherent rigidity, and slow update cycles, making them inadequate for dynamic urban environments. This paper presents a novel lightweight collaborative mapping architecture that enables real-time map updates through multi-agent cooperation. Our approach combines Joint Compatibility Branch and Bound (JCBB) for data association, Dempster-Shafer Theory (DST) for uncertainty quantification and landmark classification, and Extended Kalman Filter (EKF) for landmark pose estimation. Experimental validation using the CARLA simulator demonstrates accurate landmark classification and localization. Furthermore, collaborative data fusion reduces false positives and improves overall system reliability.