Test Results of Collision Warning Systems for Surface Mining Dump Trucks

Details

• Personal Author:
  Ruff, Todd M.
• Corporate Authors:
  National Institute for Occupational Safety and Health. Pittsburgh Research Laboratory
• Description:
  "An average of 13 mine workers are killed each year by being runover or pinned by mobile mining equipment. At surface mines, these accidents commonly involve large dump trucks that drive over a smaller vehicle or a person that is in the dump truck's blind spot. One method of detecting a person or another vehicle in a blind spot is to use some type of sensor technology such as radar or radiofrequency identification (RFID). Researchers at the Spokane Research Laboratory of the National Institute for Occupational Safety and Health tested a number of commercially available and experimental sensors that monitor obstacles in a vehicle's blind spots. None of the sensors had been previously applied to the specific problem of rigid-frame surface mining trucks. This report documents the procedures and results of tests conducted after RFID and radar systems were mounted on a 50-ton-capacity dump truck. It was determined that both RFID and radar technology show promise for detecting obstacles in the blind spots of mining equipment
  
  however, more development work is needed to meet the unique requirements of mining equipment and the mine environment. " - NIOSHTIC-2
  
  
• Content Notes:
  Includes bibliographical references (p. 41).
• Subjects:
  Electrical Equipment And Supplies Occupational Health Safety
• Series:
  NIOSH Numbered Publications
• Subseries:
  Report of Investigations
• DOI:
  https://doi.org/10.26616/NIOSHPUB2000120
• Document Type:
  Text
• Genre:
  Report
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  print ; 44 p. : ill. ; 28 cm.
• NIOSHTIC Number:
  nn:20000652
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:532287ee2c20a081adfe4235b32ae022af0819c11eb7fd20f17d8f846e750a766f7c5c7e63b2daebe62a19bb6f9397e31f5de8115927241e8a05e6da73043f32
• Download URL:
  https://stacks.cdc.gov/view/cdc/6395/cdc_6395_DS1.pdf
• File Type:
  [PDF - 5.54 MB ]