A Distributed Storage and Fusion Management Method of Massive Vehicle Multi-Source Sensor Data

With the advancement of sensor technology, an increasing amount of multi-source vehicle sensor data has emerged, offering significant value in areas such as high-precision navigation and intelligent driving. In this article, a fused organization expression model for vehicle multi-source sensor data is proposed, name V-MSFOEM. Based on the V-MSFOEM and combined with a distributed database, query algorithms such as spatial nearest-neighbor point query, spatio-temporal range query, and fused point data based on interest sections are proposed. The public dataset KITTI is used to compare the proposed algorithm with the traditional query methods, and the experimental results demonstrate that the proposed algorithms exhibit higher query efficiency, providing flexible and efficient foundational data support for upper-layer applications.