A Novel Background Filtering Method With Automatic Parameter Adjustment for Real-Time Roadside-LiDAR Sensing System

Zhihui Chen, Hao Xu, Junxuan Zhao, Hongchao Liu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Roadside-light detection and ranging (LiDAR) sensing system can provide the full trajectories of all-type road users around the deployed traffic facility, which is a new-generation traffic data to assist traffic safety and operation applications. Background filtering is a critical step of roadside-LiDAR data processing that significantly affects processing quality and efficiency. Existing background filtering methods heavily rely on statistical or empirical approaches for model parameter determination, so they normally work well for some scenarios but cannot accommodate others due to different traffic characteristics. In this article, a novel background filtering method is developed, whose model parameters can be automatically determined with the site's traffic-related measurements. The new method is designed to work on a ranging image data structure derived from the spherical features of the LiDAR sensor. The performance evaluations are conducted at three signalized intersections equipped with 32-line LiDAR sensor roadside-LiDAR under 10-Hz operational frequency, which demonstrated that the developed method can guarantee a high background filtering accuracy with more underlying foreground points detected while simultaneously achieving a significantly higher processing efficiency in comparison with existing methods.

Original languageEnglish
Article number5022310
JournalIEEE Transactions on Instrumentation and Measurement
Volume72
DOIs
StatePublished - 2023

Keywords

  • Adaptive parameters
  • background filtering
  • light detection and ranging (LiDAR) sensing system
  • roadside-LiDAR
  • traffic flow feature

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