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Nericell: Rich Road and Traffic Monitoring using Mobile Smartphones

Nericell: Rich Road and Traffic Monitoring using Mobile Smartphones. Prashanth Mohan Venkat Padmanabhan Ram Ramjee Microsoft Research India, Bangalore Presented by Ali Khodaei. Traffic Monitoring. Developed Countries GPS based tracking is adequate Infrastructure support exists

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Nericell: Rich Road and Traffic Monitoring using Mobile Smartphones

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  1. Nericell: Rich Road and Traffic Monitoring using Mobile Smartphones Prashanth Mohan Venkat Padmanabhan Ram Ramjee Microsoft Research India, Bangalore Presented by Ali Khodaei

  2. Traffic Monitoring • Developed Countries • GPS based tracking is adequate • Infrastructure support exists • Developing Countries • Bangalore ≠ <your favorite US city>

  3. What’s Different? • Potholes • Road bumps • Varied vehicle types • Liberal honking • Chaotic intersections • …

  4. Why Rich Monitoring? Find least stressful route

  5. Widespread distribution of mobile phones • Road and Traffic Monitoring • Without deployed infrastructure • Using existing mass of mobile phones

  6. Mobile Phones • ~3 billion phones worldwide • ~300 million phones in India • 115 million of 1 billion phones sold worldwide in 2007 were smartphones • 7 million added in India each month

  7. Mobile Phones • Mobility • Computing + communication + sensing • Far more capable & ubiquitous than special purpose sensors

  8. Nericell Overview • Using sensors individually • Accelerometer • drive quality • Microphone • honking • GSM radio and GPS • localization • Using sensors in combination • Virtual reorientation of accelerometer • Distinguishing pedestrians from stop-and-go traffic • Triggered sensing

  9. Energy Challenge Measurements made on HP iPAQ hw6965 9

  10. Accelerometer-based Sensing • Advantage: low energy cost • Challenge: “disorientation” • Analyses: • braking detection • bump/pothole detection • pedestrian versus stop-and-go traffic

  11. Braking Detection • Braking impacts drive quality • Two approaches: • GPS: high energy cost (600 mW on iPAQ hw6965) • Accelerometer: much cheaper (2 mW + 30mW) • Accelerometer-based braking detection:

  12. Braking Detection • braking would cause a surge in aX because the accelerometer would experience a force pushing it to the front. • To detect the incidence of braking • compute the mean of aX over a sliding window N seconds wide. • If the mean exceeds a threshold T, that event is declared as a braking event

  13. Virtual Reorientation

  14. Virtual Reorientation • Euler Angles: • Any orientation of the accelerometer can be represented by Z-Y-Z (and other equivalent) rotations • Three Unknowns (angles): • pre-rotation (φpre ), • pre tilt (θtilt), • post-rotation (ψpost) • Knowns: • Gravity along Z • Braking along X

  15. Euler Angles

  16. Virtual ReorientationUsing Gravity

  17. Using Braking

  18. Automatic Virtual Reorientation

  19. Results: Virtual Reorientation

  20. Braking detection with Virtual Reorientation

  21. Differentiating pedestrians from stop-and-go traffic

  22. Road bump detection

  23. Locating a Road Bump • Accelerometer is cheap, so keep on continuously • When bump is detected, use GSM tower matching to estimate location • median error: 130 m, 90th percentile: 610 m • Send bump report to server • If several reports in same vicinity, server triggers GPS on other phones for location fix • Sample result: GPS turned on only 3.2% of the time on a 20 km drive with one point of interest

  24. Pothole Detection

  25. Pothole Detection High speed (≥ 25 kmph) z-peak: look for significant spike

  26. Pothole Detection

  27. Pothole Detection • Low speed (< 25 kmph) • z-sus: look for sustained dip

  28. Results: Pothole Detection • Training data: 5km long drive with 44 bumps • Test data: 35km long drive with 101 bumps

  29. Microphone-based Sensing • Advantage: ubiquity • Challenge: energy, privacy • Analyses: • honk detection: triggered when accelerometer indicates a lot of braking • vehicle type: exposed versus enclosed vehicle

  30. Honk Detection • Efficient detector suitable for mobiles • discrete Fourier transform on 100 ms of audio • look for spikes in the 2.5-4 kHz range • spikes at least T times the mean, where T ranges between 5 and 10 • Performance: 5.8% of CPU on the HP iPAQ

  31. Honk Detection

  32. Triggered Sensing • Use cheap sensors to trigger the activation of expensive sensors when needed • Examples: • Virtual Reorientation: accelerometer -> GPS • Localization: GSM -> GPS • Honk detection: accelerometer -> audio

  33. Resources • Trafficsense: Rich monitoring of road and traffic conditions using mobile smartphones • P Mohan, VN Padmanabhan, R Ramjee - SenSys'08 • http://research.microsoft.com/research/mns

  34. Questions?!

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