phase I.

Details

• Creators:
  Druta, Cristian ; Alden, Andrew S.
• Corporate Creators:
  Virginia Tech Transportation Institute
• Contributors:
  Guevara, Gabriel
• Corporate Contributors:
  United States. Federal Highway Administration ; United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office
• Subject/TRT Terms:
  • Weather conditions
  • Slipperiness
  • Wheel slip
  • In vehicle sensors
  • Risk assessment
  • Traction
  • Pavement friction
  • wheel sensor pulse
  • weather conditions
  • rotational displacement
  More +
• Publication/ Report Number:
  FHWA-JPO-16-359
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States;Virginia
• Edition:
  Final report
• Corporate Publisher:
  United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office
• Abstract:
  Real-time assessment of road surface conditions can be used to provide valuable safety information to travelers when roads are slippery (e.g., ; contaminated roads, heavy rain, black ice, etc.). Identification of these hazardous surface conditions using onboard vehicle sensors will warn drivers to ; proceed with caution on compromised road sections, thus reducing the risks of crashes. This information may also be used to provide data on roadway ; deficiencies such as winter weather impacts and wear loss of surface friction to highway agencies resulting in more efficient and less costly ; maintenance operations. ; The main objective of this study was to establish a real-time relationship between certain variables collected by vehicle onboard sensors and the ; roadway surface conditions. It is hypothesized that the relative difference in rotation between the driven and nondriven wheels may be used to assess ; pavement surface condition, and thus, traction. ; Front- and rear-wheel drive vehicles were tested to determine the relative rotational displacements of driven and nondriven wheels under dry, wet, ; snowy/slushy, and icy road surface conditions. These variables, among others, were supplied by the factory-installed wheel-speed sensors utilized by ; the vehicle’s Anti-lock Braking System (ABS). The rear-wheel drive vehicle was driven over two pavement sections of different grades and lengths to ; compare results. The front-wheel drive vehicle was driven on a single pavement section at the Virginia Tech Transportation Institute’s Smart Road ; facility. Both vehicles were driven under controlled conditions of constant speed, minimal steering, no braking, and with monitoring of onboard safety ; systems (e.g., ABS, stability control) as potential confounds. Time and position data were collected from a Differential Global Positioning System ; (DGPS) installed in the vehicles. All these data were employed to calculate total distances traveled by the vehicles as well as ratios between distances ; traveled by driven and nondriven wheels in order to distinguish between different pavement surface conditions. ; The results of experimentation with multiple test runs conducted on roads conditions ranging from dry to icy showed a small but statistically discernable ; difference in the relative rotational displacement of driven versus nondriven wheels. Changes in the observed rotation ratios were clearly associated ; with pavement conditions known to produce poor traction (e.g., icy, slightly wet and dirty). That is, tests performed on slippery roads resulted in an ; increased change in the driven versus nondriven wheel rotational rates. Of the surface condition scenarios tested, icy, and slightly wet and dirty, and ; certain snow-covered pavement provided the least traction while clean dry, moderately wet, and other snow-covered pavement conditions provided ; better traction.
  More +
• Format:
  PDF
• Funding:
  DTFH61-13-P-00017
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/32155/dot_32155_DS1.pdf
• File Type:
  [PDF - 2.25 MB]
• Collection(s):
  US Transportation Collection ; Federal Highway Administration
• Main Document Checksum:
  urn:sha-512:7f6f47da91f3b29b90c924a3718534ac8deb37dd77d84092dd6917b84c91cc52bf4abf0b6a3dc14572a8549aadcbf34e6667b434daa095712229bfbb7f19983a