Paper Info
Title
Automatic Vehicle Detection With Roadside LiDAR Data Under Rainy and Snowy Conditions
Abstract
The previous studies showed that rainy and snowy weather can reduce the quality of LiDAR data. In rainy and snowy weather, laser beams of LiDAR were often blocked by raindrops or snowflakes, which was called weather occlusion. The vehicle detection with weather occlusion is a challenge. When the traditional density-based spatial clustering of applications with noise (DBSCAN) was used for vehicle clustering, the data processing showed that the false detection rate of the conventional DBSCAN under the snowy weather was high. This paper aims to present the characteristics of roadside LiDAR data in snowy and rainy days and improve the accuracy of vehicle detection during challenging weather conditions. A revised DBSCAN method named 3D-SDBSCAN is raised up to distinguish vehicle points and snowflakes in the LiDAR data. Adaptive parameters were applied in the revised DBSCAN method to detect vehicles with different distances from the roadside LiDAR sensor. The performance of the proposed method and the conventional DBSCAN algorithm were compared using the data collected under rainy and snowy conditions. The results showed that the 3D-SDBSCAN algorithm could overcome weather occlusion issue better than the conventional one.
Year
DOI
Venue
2021
10.1109/MITS.2019.2926362
IEEE Intelligent Transportation Systems Magazine
Keywords
DocType
Volume
automatic vehicle detection,roadside LiDAR data,rainy,snowy conditions,snowy weather,vehicle clustering,data processing,false detection rate,challenging weather conditions,revised DBSCAN method,distinguish vehicle points,roadside LiDAR sensor,conventional DBSCAN algorithm,weather occlusion issue
Journal
13
Issue
ISSN
Citations 
1
1939-1390
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Jianqing Wu100.34
Hao Xu21212.74
Jianying Zheng381.97
Junxuan Zhao441.45