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.

Keywords:

• lane identification
• roadside LiDAR
• traversal search

Additional information

Funding

This research was funded by the SOLARIS Institute, a Tier 1 University Transportation Center (UTC) under Grant No. (DTRT13-G-UTC55), the Nevada Department of Transportation (NDOT) under Grant No. (P224-14-803/TO#13), Natural Science Foundation of China (No. 61463026, 61463027), and Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University. This research was also supported by engineers with the Nevada Department of Transportation, the Regional Transportation Commission of Washoe County, Nevada, and the City of Reno.