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This research paper employed a multi-session framework to present an innovative approach to map monitoring within the domain of high-definition (HD) maps. The proposed methodology uses a machine learning algorithm to derive a confidence level for the detection of specific map elements in each frame and tracks the position of the element in subsequent frames. This creates a virtual belief system, which indicates the existence of the element on the HD map. To confirm the existence of the element and ensure the credibility of the map data, a reconstruction and matching technique was implemented. The notion of an expected observation area is also introduced by strategically limiting the vehicle’s observation range, thereby bolstering the detection confidence of the observed map elements. Furthermore, we leveraged data from multiple vehicles to determine the necessity for updates within specific areas, ensuring the accuracy and dependability of the map information. The validity and practicality of our approach were substantiated by real experimental data, and the monitoring accuracy exceeded 90%.