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Prediction of Roadway Surface Conditions Using On-Board Vehicle Sensors

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Prediction of Roadway Surface Conditions Using On-Board Vehicle Sensors. Andy Alden Group Leader – VA Green Highway Initiative Virginia Tech Transportation Institute ITSVA 2014 Conference February 18, 2014. Project Information. Research Objective.

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slide1

Prediction of Roadway Surface Conditions Using On-Board Vehicle Sensors

Andy Alden

Group Leader – VA Green Highway Initiative

Virginia Tech Transportation Institute

ITSVA 2014 Conference

February 18, 2014

slide2

Project Information

ITSVA - June 6, 2014

slide3

Research Objective

  • Predict road surface friction in real time using the relative rotational displacement rates of vehicle wheels
    • Use the Smart Road facility to collect relevant data from test vehicles under specific weather and roadway conditions
    • Demonstrate how this data would be used in Connected Vehicle safety and maintenance applications
    • Support FHWA efforts to support requests for CAN bus data for inclusion in BSMs

ITSVA - June 6, 2014

slide4

Traction Primer

  • Friction forces = forces applied to tires
  • Effective rolling radius
  • Longitudinal slip (reffωw -Vx)

(net velocity)

  • Microslip
  • Macroslip
  • Rolling resistance = Loss of energy (opposed to Vx)
  • Tire/Road parameters effects

ITSVA - June 6, 2014

slide5

Wheel Rotation Characteristics of a Moving Vehicle

  • Slip results in under or over rotation of wheel with respect to vehicle distance traveled
  • Opposing slip effects at the wheels (V = constant)
  • Traction loss (slip) leads to drive wheels rotate more than non-driven (free-rolling) wheels

ITSVA - June 6, 2014

slide6

Traction Prediction Concept

Slip is the under- or over-rotation of wheel with respect to vehicle distance traveled

Where: PDat drive wheel

PF = pulses at free-rolling wheel

As traction

Comparison of relative rotation of driving versus freerolling wheels >>>>> Traction

ITSVA - June 6, 2014

slide7

Methodology – Vehicle

2008 Chevrolet Tahoe

ITSVA - June 6, 2014

slide8

Methodology - Vehicle Instrumentation

    • NextGen data acquisition system (DAS)
  • Controller area network (CAN) bus interface module (for communication inside the vehicle)
    • Head unit incorporating an inertial measurement unit (IMU)
  • Differential GPS (DGPS)
  • Network box (interfaces with the vehicle on-board computer)

ITSVA - June 6, 2014

slide9

Methodology – Test Site

The Virginia Smart Road

ITSVA - June 6, 2014

slide10

Targeted Test Conditions

ITSVA - June 6, 2014

slide11

Methodology – Test Controls

  • Constant speed (35 mph)
  • Middle of the lane (minimal steering)
  • Cruise control (less speed variation)
  • No braking
  • Monitor tire and weather
  • Geofencing for DGPS

ITSVA - June 6, 2014

slide12

Methodology – Data Collected

  • GPS time and position — With real-time differential correction
  • Wheel rotation sensor pulse counts at all wheels from the CAN bus.
  • Status of ABS, ESC, and TSC from the CAN bus.
  • Brake activation and applied torque at all wheels.
  • Throttle, both applied and actual.
  • 3 Axis linear acceleration.
  • Network variables indicative of weather (temperature, atmospheric pressure, windshield wiper and headlight activation, etc.)

ITSVA - June 6, 2014

slide13

Results

ITSVA - June 6, 2014

slide14

Results

ITSVA - June 6, 2014

slide15

Results – T-test and ANOVA

ITSVA - June 6, 2014

slide16

Lessons Learned

  • We can identify changing road friction using on-board sensors.
    • We can predict relative friction levels but association with condition may be problematic (e.g. snow versus ?)
  • The traction provided by snow and other frozen precipitation varies greatly with characteristics.
  • Water on dirty roads makes for slippery conditions
  • Front wheel drive vehicles may provide the best data
  • We may need to protect intellectual property (MDSS)
  • The real hazards are probably those not readily apparent – rain/snow versus black ice, hydroplaning, dirty roads

ITSVA - June 6, 2014

slide17

Future Related Work

  • Integration within Connected Vehicle for:
    • Real time safety applications
    • Winter maintenance optimization
  • On–board vehicle sensors used for:
    • Fog/smoke detection
    • Wind gust detection
    • CBERN - Chemical, Biological, Explosive, Radiological and Nuclear (with additional sensors)
    • Pedestrian/Animal in the Roadway Detection

ITSVA - June 6, 2014

slide18

Questions? Comments?

Contact Info

Andrew (Andy) Alden Email: aalden@vt.edu

www.vtti.vt.edu 540-231-1526

  • Other Ongoing Projects
    • Evaluation of Salt-Rich Biochar as a Roadway De-icing Agent in Support of the Recycling of Applied Road Salts through Phytoremediation and Bio-Fuel Production
    • Naturalistic Bicycle Crash Causation
    • Real Time Transit Bus Passenger Demand Assessment and Adaptive Routing/Scheduling
    • Roadside Animal Detection for Potential Integration with CVI
    • Vehicle-based Animal Detection Using On-Board Sensors

ITSVA - June 6, 2014