On Software-based Remote Vehicle Monitoring for Detection and Mapping of Slippery Road Sections

The use of vehicular communications and networking technology has started to open up endless possibilities for applications development. These efforts include a wide rage of applications from road maintenance and traffic engineering at the transportation engineering level to applications designed for personal convenience and entertainment. In this paper, we describe our project on monitoring road conditions supported by the Department of Transportation Research Innovation and Technology Administration (DOT-RITA) program and the various agencies in Anchorage, Alaska. Our project first focuses on identifying dangerous road sections typically caused by, but not limited to, ice patches using vehicle slippage detection. The detection was done through probe vehicles equipped with our custom vehicular interface and networking device called CANOPNR (CAN-BUS OBD Programmable-expandable Network-enabled Reader). We describe the details of the CANOPNR architecture as well as our algorithm for slippage detection and present our test results. Along with the hardware configuration, our software has also been made openly available for the research community to use. Our test results showed that the system is able to effectively identify slippery road sections. We were also able to show the potential for prediction of some accident-prone areas. Individual cities may significantly benefit from utilizing such methodology with existing service vehicles as well as with the participation by the public. The end-users can benefit from safer-route computations enabled by the mappings.

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    • Copyright © 2017, Springer. The contents of this paper reflect the views of the author[s] and do not necessarily reflect the official views or policies of the Transportation Research Board or the National Academy of Sciences.
  • Authors:
    • Enriquez, Denrie
    • Jenson, Sean
    • Bautista, Alex
    • Hawn, Paloma
    • Kim, Sun-il
    • Ali, Muhammad
    • Miller, Jeffrey
  • Publication Date: 2017-9


  • English

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  • Accession Number: 01642352
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 27 2017 10:05AM