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Vehicle Infrastructure Integration (VII): Scientific Challenges

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Presentation on theme: "Vehicle Infrastructure Integration (VII): Scientific Challenges"— Presentation transcript:

1 Vehicle Infrastructure Integration (VII): Scientific Challenges
Kevin R. Petty Bill P. Mahoney Richard R. Wagoner National Center for Atmospheric Research VII Weather Applications Workshop Boulder, Colorado Feb. 22, 2006

2 Vehicle Infrastructure Integration (VII)
BENEFIT: VII enables tactical and strategic response to weather related surface transportation hazards. Synoptic Scale Highs, Lows, Fronts Nor’easters Hurricanes, tropical storms Thunderstorms Mesoscale Tornadoes Microscale Black ice, Fog

3 VII Weather Application Development: Expected Challenges
Data comprehension Data quality and accuracy Data volume Spatial distribution Complex terrain Data fusion Quality checking Algorithm and Concept Development

4 Data Quality and Accuracy
Sensor placement is likely to result in variability in certain parameters (e.g. temperature, rain, sun)

5 Data Quality and Accuracy
Sensor type can also lead to differences in measured parameters: Range Data type Precision Resolution Accuracy Account for biases Vehicle knowledge Privacy Rain Sensor Suppliers: Bosch Denso Kostal TRW Valeo Thermister Thermocouple

6 Data Volume Determine how to utilize large amounts of data Averaging
Wipers=on/high Averaging Over distance Over time Equivalent to a point measurement (replicating ASOS) 28°F 36°F Wipers=off Wipers=on/low 35°F Wipers=off

7 Urban Versus Rural Environments
A major challenge exists in terms of rural regions. How do we create applications that work equally well in urban and rural environments? 38°F Wipers=on/high 28°F 36°F Wipers=off Wipers=on/low Regional car types 35°F Wipers=off

8 Urban Versus Rural Coverage
Population Centers Snow/Ice Light rain Thunderstorms Heavy rain Tornadoes Dust storms Fog

9 Complex Terrain Data variations Elevation Terrain separation
Quality checking

10 Data fusion 6 mins./10 mins. Disparate Data Processing time
Algorithm initiation intervals 15 minutes 1 hr with specials Model Dependent 38°F 28°F 36°F 35°F

11 Quality Checking What is truth?
28°F 38°F Quality checks on mobile platform data (Clarus like) Sensor test range Spatial test (terrain) Climatology Persistence (privacy) Step test 36°F 35°F Mobile Sensors ASOS/AWOS What is truth? Radar Satellite


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