Innovative vehicle classification strategies : using LIDAR to do more for less.

Details

• Alternative Title:
  USDOT Region V Regional University Transportation Center Final Report
• Creators:
  Coifman, Benjamin ; Lee, Ho
• Corporate Creators:
  Ohio State University. Department of Civil and Environmental Engineering and Geodetic Science ; Ohio State University. Dept. of Electrical and Computer Engineering ; NEXTRANS Center (U.S.)
• Corporate Contributors:
  Purdue University ; United States. Department of Transportation. Research and Innovative Technology Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Vehicle classification
  • Innovation
  • Planning and design
  • Strategic planning
  • Laser radar
  • Radar
  • Vehicle classification
  • Innovation
  • Planning and design
  • Strategic planning
  More +
• Publication/ Report Number:
  NEXTRANS Project No 033OY02
• Resource Type:
  Tech Report
• Geographical Coverage:
  Ohio
• Edition:
  Final report.
• Corporate Publisher:
  NEXTRANS Center (U.S.)
• Abstract:
  This study examines LIDAR (light detection and ranging) based vehicle classification and classification ; performance monitoring. First, we develop a portable LIDAR based vehicle classification system that can ; be rapidly deployed, and then we use the LIDAR based system for automated validation of conventional ; vehicle classification stations. ; We develop the LIDAR based classification system with the sensors mounted in a side-fire configuration ; next to the road. The first step is to distinguish between vehicle returns and non-vehicle returns. The ; algorithm then clusters the vehicle returns into individual vehicles. The algorithm examines each vehicle ; cluster to check if there is any evidence of partial occlusion from another vehicle. Several measurements ; are taken from each non-occluded cluster to classify the vehicle into one of six classes: motorcycle, ; passenger vehicle, passenger vehicle pulling a trailer, single-unit truck, single-unit truck pulling a trailer, ; and multi-unit truck. The algorithm was evaluated at six different locations under various traffic ; conditions. Compared to concurrent video ground truth data for over 27,000 vehicles on a per-vehicle ; basis, 11% of the vehicles are suspected of being partially occluded. The algorithm correctly classified ; over 99.5% of the remaining, non-occluded vehicles. This research also uncovered emerging challenges ; that likely apply to most classification systems, e.g., differentiating commuter cars from motorcycles.
  More +
• Format:
  PDF
• Funding:
  DTRT07-G-005
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24672/dot_24672_DS1.pdf
• File Type:
  [PDF - 2.70 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:c9746da6090e5bdbc47cb945101f8bc1a68cb6b5d45a65fbed518c88e1447678