Paper Info
Title
Automatic Background Filtering And Lane Identification With Roadside Lidar Data
Abstract
The high-resolution traffic data (HRTD) of all roadway users is important to connected-vehicle systems, traffic safety analysis, performance evaluation and fuel efficiency study. The roadside LiDAR (light detection and ranging) sensors can provide HRTD by collecting real-time 3D point clouds of surrounding objects, which is significant to connected-vehicle applications with mixed traffic flow connected and unconnected road users. The background filtering is a necessary step to improve the accuracy and efficiency of HRTD extraction from raw LiDAR data. At the same time, the lane space information can further enhance the accuracy and reliability of data extraction. This paper presents an algorithm to extract background automatically from the LiDAR data based on the density of points in 3D space; and a multi-classified density-based spatial clustering method (MC-DBSCAN) is developed to identify road lanes automatically. The boundaries of road lanes are automatically located with aggregated trajectories of vehicles. The algorithms were applied for roadside LiDAR data preprocessing at different sites. Data and results from one site are presented in this paper.
Year
Venue
Keywords
2017
2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Roadside LiDAR, connected vehicle, background extraction, lane identification
Field
DocType
ISSN
Computer vision,Traffic flow,Filter (signal processing),Preprocessor,Ranging,Lidar,Artificial intelligence,Data extraction,Engineering,Point cloud,Cluster analysis
Conference
2153-0009
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jianqing Wu142.46
Hao Xu21212.74
jianying zheng3276.67