Traffic light positioning method based on multiple clustering

Abstract

The invention relates to a traffic light positioning method based on multiple clustering. The method comprises the steps of step 1, collecting traffic light data in an area; step 2, grouping the traffic light data to obtain data sets corresponding to different heading directions; step 3, respectively partitioning the data of each data set to obtain different acquisition vehicles in each data set and data blocks corresponding to acquisition time periods; step 4, traversing each data set, determining the number of traffic lights in the data set based on a DBSCAN clustering algorithm, taking the obtained number of traffic lights as the number of clustering centers, and determining the number of traffic lights in the data set based on K-based on a K-based clustering algorithm; and performing Means clustering calculation to obtain a clustering center which is a traffic light coordinate result. Based on the DBSCAN clustering algorithm, the number of traffic lights in the data set is determined, and the number of traffic lights obtained is the number of cluster centers, and the cluster center is obtained as a traffic light coordinate result based on K-Means clustering calculation. Based on the DBSCAN clustering algorithm, the number of traffic lights is searched, and the K-Means clustering algorithm is used to find the clustering center multiple clusters. The accuracy and positioning accuracy of the traffic lights are improved compared with the traditional single clustering.

Description

technical field [0001] The invention relates to the field of map generation under crowdsourcing mode, in particular to a traffic light positioning method based on multiple clustering. Background technique [0002] Among various map service technologies, whether the position of traffic lights on the map is accurate is of great significance to map navigation and assisted driving. [0003] The traffic light positioning method in the prior art is mainly to first identify the traffic light with a camera, and then rely on the radar to detect the relative position of the traffic light and the car and calculate the GPS coordinates of the car itself. However, the recognition accuracy of traffic lights detected by radar is not 100%, and the position of traffic lights needs to be calculated by the car's own GPS after recognition. Due to the possible error between GPS and relative position, the accuracy of traffic lights will be poor. . [0004] In addition, when the error of the coll...

AI Technical Summary

Problems solved by technology

However, the recognition accuracy of traffic lights detected by radar is less than 100%, and the position of traffic lights needs to be calculated by the car's own GPS after recognition. Due to the possible error between GPS and relative position, the accuracy of traffic lights will be poor.

[0004] In addition, when the error of the collected data is large, direct clustering using traditional clustering algorithms (such as the DBSCAN algorithm) will have greater instability, which may lead to inaccurate numbers of traffic lights and poor positioning accuracy

Images