Stationary LiDAR for Traffic and Safety Applications – Vehicles Interpretation and Tracking

The goal of the Traffic Scanning (T-Scan) project is to develop a data processing module for a novel Light Detection and Ranging (LiDAR)-based traffic scanner to collect highly accurate microscopic traffic data at road intersections. T-Scan uses LiDAR technology that can detect and track various types of road users, including buses, cars, pedestrians, and bicycles; and, unlike video detection, it does not experience the well-known occlusion problem. Moreover, LiDAR data has a one-to-one correspondence with the physical world, which makes it possible in principle to produce the positions and velocities of road users in real-time as needed for traffic and safety applications, with the errors of estimation dependent only on the resolution and accuracy of the LiDAR sensor. This report presents a research project that is the first step towards evaluating the feasibility and developing a practical tool of T-Scan for counting turning vehicles at intersections, measuring traffic interactions for the purpose of safety estimation, and conducting other traffic studies. The presented first phase of this research includes: 1) the integration and evaluation of a data acquisition system, 2) the development of basic pre-processing functions for data reduction, storing, and retrieval, 3) recognition and extraction of the fixed background, 4) correction of the measurements for the sensor motion, and 5) development of a concept for tracking and classifying moving objects.

• Record URL:
  http://www.purdue.edu/discoverypark/nextrans/assets/pdfs/134PY2.1%20Stationary%20LiDAR%20for%20Traffic%20and%20Safety%20Applications%20Vehicles%20Interpretation%20and%20Tracking.pdf
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/36884
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  NEXTRANS

  Purdue University
  3000 Kent Avenue
  Lafayette, IN  United States  47906-1075

  Research and Innovative Technology Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Tarko, Andrew P
  • Ariyur, Kartik B
  • Romero, Mario A
  • Bandaru, Vamsi Krishna
  • Liu, Cheng
• Publication Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 69p

Subject/Index Terms

• TRT Terms: Automatic vehicle detection and identification systems; Data collection; Data storage; Feasibility analysis; Intersections; Laser radar; Sensors; Traffic data; Turning traffic
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01544620
• Record Type: Publication
• Report/Paper Numbers: NEXTRANS Project No. 134PY2.1
• Contract Numbers: DTRT07-G-005
• Files: UTC, NTL, TRIS, RITA, ATRI, USDOT
• Created Date: Nov 24 2014 3:28PM