Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data
This work addresses the problem of efficiently creating and updating high-precision maps for autonomous vehicles, representing an incremental improvement in map technology for the intelligent transportation domain.
The paper tackles the challenge of generating high-precision GPS data for intelligent vehicles by developing an algorithm that fuses low-precision GPS trajectory data to create key data points, enabling a crowdsourced update model for map data collection. This approach improves data accuracy, reduces measurement costs, and decreases data storage space.
In recent years, the rapid development of high-precision map technology combined with artificial intelligence has ushered in a new development opportunity in the field of intelligent vehicles. High-precision map technology is an important guarantee for intelligent vehicles to achieve autonomous driving. However, due to the lack of research on high-precision map technology, it is difficult to rationally use this technology in the field of intelligent vehicles. Therefore, relevant researchers studied a fast and effective algorithm to generate high-precision GPS data from a large number of low-precision GPS trajectory data fusion, and generated several key data points to simplify the description of GPS trajectory, and realized the "crowdsourced update" model based on a large number of social vehicles for map data collection came into being. This kind of algorithm has the important significance to improve the data accuracy, reduce the measurement cost and reduce the data storage space. On this basis, this paper analyzes the implementation form of crowdsourcing map, so as to improve the various information data in the high-precision map according to the actual situation, and promote the high-precision map can be reasonably applied to the intelligent car.