Conference Proceeding

Object Detection and Identification Using Enhanced Camera/Video Imaging Systems (E-C/VISs) on Heavy Trucks

Authors
  • William A Schaudt (Virginia Tech Transportation Institute, Blacksburg, VA)
  • Robert W Wierwille (Virginia Tech Transportation Institute, Blacksburg, VA)
  • Richard J Hanowski (Virginia Tech Transportation Institute, Blacksburg, VA)

Abstract

Tests were performed to determine the feasibility of developing an Enhanced Camera/Video Imaging System (E-C/VIS) to provide heavy vehicle drivers with better situation awareness to the sides and rear of their vehicles. It is well known that large blind spots currently exist in these areas and that sideswipe crashes can occur as a result. An additional goal was to extend the operating envelope of conventional video to nighttime and to inclement weather. A three channel system was envisioned in which there would be a camera at each (front) fender of the tractor looking backward along the sides of the tractor trailer. The third channel would be aimed rearward from the back of the trailer. Indoor tests involved selection of components having the best capabilities, while early outdoor tests used the selected components in a single-channel side mounted system. Once developed, the heavy vehicle three-channel system was tested in a static object detection and identification experiment, as well as a dynamic on-road experiment. The current document describes the static object detection and identification experiment methodology and results. In regard to object detection and identification, objects were correctly detected and identified significantly more often with the E-C/VIS than with mirrors alone. Objects directly behind the heavy vehicle could be detected with the rear wide-angle look-down camera of the EC/VIS whereas such objects could not be detected with conventional side mirrors.

How to Cite:

Schaudt, W. & Wierwille, R. & Hanowski, R., (2009) “Object Detection and Identification Using Enhanced Camera/Video Imaging Systems (E-C/VISs) on Heavy Trucks”, Driving Assessment Conference 5(2009), 54-60. doi: https://doi.org/10.17077/drivingassessment.1302

Rights: Copyright © 2009 the author(s)

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Published on
23 Jun 2009
Peer Reviewed