Farm Vehicle Following Distance Estimation Using Deep Learning and Monocular Camera Images

Details

• Personal Author:
  Arabi S ; Hamann C ; Peek-Asa, Corinne ; Reyes M ; Sharma A
• Description:
  This paper presents a comprehensive solution for distance estimation of the following vehicle solely based on visual data from a low-resolution monocular camera. To this end, a pair of vehicles were instrumented with real-time kinematic (RTK) GPS, and the lead vehicle was equipped with custom devices that recorded video of the following vehicle. Forty trials were recorded with a sedan as the following vehicle, and then the procedure was repeated with a pickup truck in the following position. Vehicle detection was then conducted by employing a deep-learning-based framework on the video footage. Finally, the outputs of the detection were used for following distance estimation. In this study, three main methods for distance estimation were considered and compared: linear regression model, pinhole model, and artificial neural network (ANN). RTK GPS was used as the ground truth for distance estimation. The output of this study can contribute to the methodological base for further understanding of driver following behavior with a long-term goal of reducing rear-end collisions. [Description provided by NIOSH]
• Subjects:
  Metals Minerals Mining Occupational Health Safety
• Keywords:
  Accident Prevention Author Keywords: Safety Behavior Deep Learning Distance Estimation Drivers Driving Behavior Motor Vehicles Sensors Vehicle Safety Warning Systems

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• ISSN:
  1424-8220
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  California ; Iowa ; OSHA Region 7 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  22
• Issue:
  7
• NIOSHTIC Number:
  nn:20067646
• Citation:
  Sensors 2022 Apr; 22(7):2736
• Contact Point Address:
  Saeed Arabi, Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
• Email:
  arabi@iastate.edu
• Federal Fiscal Year:
  2022
• NORA Priority Area:
  Agriculture, Forestry and Fishing
• Performing Organization:
  University of Iowa, Iowa City
• Peer Reviewed:
  True
• Start Date:
  20010930
• Source Full Name:
  Sensors
• End Date:
  20270929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:45a241db4fbfcb51052dbffd9ba720c7337dc97e87b385ca219622f4896a47fc46bb09ff69db975b5246753b69aca6ba98e45e848e7a2feb3cd8964043fdd4ed
• Download URL:
  https://stacks.cdc.gov/view/cdc/229918/cdc_229918_DS1.pdf
• File Type:
  [PDF - 3.90 MB ]