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Prediction of Roadway Surface Conditions Using On-Board Vehicle Sensors

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

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  1. 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

  2. Project Information ITSVA - June 6, 2014

  3. 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

  4. 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

  5. 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

  6. 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

  7. Methodology – Vehicle 2008 Chevrolet Tahoe ITSVA - June 6, 2014

  8. 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

  9. Methodology – Test Site The Virginia Smart Road ITSVA - June 6, 2014

  10. Targeted Test Conditions ITSVA - June 6, 2014

  11. 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

  12. 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

  13. Results ITSVA - June 6, 2014

  14. Results ITSVA - June 6, 2014

  15. Results – T-test and ANOVA ITSVA - June 6, 2014

  16. 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

  17. 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

  18. 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

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