Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data

TL;DR

This paper presents a novel crowdsourcing map construction method using multi-track GPS data fusion to generate high-precision maps, enhancing autonomous vehicle navigation with improved accuracy and reduced costs.

Contribution

It introduces a fast algorithm for fusing low-precision GPS data into high-precision map points and analyzes the implementation of crowdsourcing map updates for intelligent vehicles.

Findings

Improved GPS data accuracy through fusion algorithm

Reduced data measurement and storage costs

Enhanced applicability of high-precision maps in autonomous driving

Abstract

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.