Title
An automatic lane identification method for the roadside light detection and ranging sensor
Abstract
The roadside Light Detection and Ranging (LiDAR) sensor can provide the high-resolution micro traffic data (HRMTD) of all road users by collecting real-time point clouds in three-dimensional (3D) space. The HRMTD collected by the roadside LiDAR provides a solution to fill the data gap under the mixed situation (both connected vehicles and unconnected vehicles exist on the roads) for connected vehicle technologies. Lane identification is important information in HRMTD. The current lane identification algorithms are mainly developed for autonomous vehicles, which could not be directly used to process roadside LiDAR data. This article provides an innovative algorithm to automatically identify traffic lanes for the roadside LiDAR data. The proposed lane identification algorithm includes five major steps: background filtering, point clustering, object classification, frame aggregation, and traversal search. The parameters used in the algorithm are selected by balancing the time cost and the accuracy. With the GPS information, the location of the lane can be transferred into the GoogleEarth and be compared with the location of the lane in real world. The testing results showed that the average distance error (ADE) compared to the real location in Google Earth was less than 0.1 m. This robust lane identification can release engineers from the manual lane identification task and avoid any error caused by manual work. The extracted lane locations can be used for researchers and practitioners to locate the vehicles precisely in different applications.
Year
DOI
Venue
2020
10.1080/15472450.2020.1718500
Journal of Intelligent Transportation Systems
Keywords
DocType
Volume
lane identification,roadside LiDAR,traversal search
Journal
24
Issue
ISSN
Citations 
5
1547-2450
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jianqing Wu100.34
Hao Xu21212.74
Yuan Tian368.99
Yongsheng Zhang420443.58
Junxuan Zhao500.34
Bin Lv600.34