High-Resolution Micro Traffic Data From Roadside LiDAR Sensors for Connected-Vehicles and New Traffic Applications

Details

• Creators:
  Xu, Hao ; Tian, Zong ; Wu, Jianqing ; Liu, Hongchao ; Zhao, Junxuan
• Corporate Creators:
  University of Nevada, Reno. Solaris University Transportation Center
• Corporate Contributors:
  University of Nevada, Reno. Department of Civil and Environmental Engineering ; Nevada. Department of Transportation ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  Algorithms Connected Vehicles Data Processing Operations Laser Radar Pilot Studies Roadside Traffic Data Vehicle Trajectories
• Publication/ Report Number:
  P224-14-803 TO15
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final report
• Corporate Publisher:
  University of Nevada, Reno. Solaris University Transportation Center
• Abstract:
  This report documents and presents algorithms and procedure developed for extracting high-accuracy high-resolution trajectory data from roadside LiDAR sensors. The developed methods were evaluated with data from various traffic scenarios. Pilot applications of roadside LiDAR trajectory data for pedestrian-crossing-road prediction, animal-crossing­-road detection, and near-crash events identification were also included in this report. The roadside-LiDAR data-processing procedure includes new algorithms of LiDAR-data background filtering, LiDAR-point clustering, cluster classification (vehicles and pedestrians), object tracking and trajectory calculation. The methods for processing roadside LiDAR and pilot applications of using LiDAR trajectory data will serve as a foundation for new connected/autonomous traffic infrastructure advanced by 360-degree edge LiDAR sensors. Road-side LiDAR is new technology to fill the data gap of unconnected multimodal traffic in connected and autonomous traffic systems and will innovate traffic engineering/research areas with all-traffic trajectory data that was not available in traditional traffic sensing systems.
• Format:
  PDF
• Funding:
  DTRT13-G-UTC55;P224-14-803/TO #15
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:3c3313481f17c6cc7bcc8894af9a398e32bcbccbc1e3a65d531c1e6d0e93f6b2dc5c17be50c58375c2b58fb4fd63acf43b1d8749fb8daf5e73566fa113b9c7ea
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/44347/dot_44347_DS1.pdf
• File Type:
  [PDF - 3.13 MB ]