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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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 Vehicle Sensors

ITSVA - June 6, 2014


Research Objective Vehicle Sensors

  • 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


Traction Primer Vehicle Sensors

  • 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


Wheel Rotation Characteristics of a Moving Vehicle Vehicle Sensors

  • 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


Traction Prediction Concept Vehicle Sensors

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


Methodology – Vehicle Vehicle Sensors

2008 Chevrolet Tahoe

ITSVA - June 6, 2014


Methodology - Vehicle Instrumentation Vehicle Sensors

  • 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


    Methodology – Test Site Vehicle Sensors

    The Virginia Smart Road

    ITSVA - June 6, 2014


    Targeted Test Conditions Vehicle Sensors

    ITSVA - June 6, 2014


    Methodology – Test Controls Vehicle Sensors

    • 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


    Methodology – Data Collected Vehicle Sensors

    • 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


    Results Vehicle Sensors

    ITSVA - June 6, 2014


    Results Vehicle Sensors

    ITSVA - June 6, 2014


    Results – T-test and ANOVA Vehicle Sensors

    ITSVA - June 6, 2014


    Lessons Learned Vehicle Sensors

    • 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


    Future Related Work Vehicle Sensors

    • 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


    Questions? Vehicle Sensors Comments?

    Contact Info

    Andrew (Andy) Alden Email: [email protected]

    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


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