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.

利用高精度地图技术和人工智能，该论文研究了从大量低精度 GPS 轨迹数据融合中生成高精度 GPS 数据的快速有效算法，并基于大量社交车辆实现了基于众包更新的地图数据集合模型，从而提高数据精度、降低测量成本以及减少数据存储空间，推动高精度地图在智能车辆领域的合理应用。