1. The Pothole Patrol: Using a Mobile Sensor Network for Road Surface Monitoring Jakob Eriksson, Lewis Girod, Bret Hull, Ryan Newton, Samuel Madden, Hari Balakrishnan MIT Computer Science and Artificial Intelligence Laboratory Mobisys 2008 Speaker: Lawrence 1

2. Outline Introduction P 2 Architecture Data Acquisition Algorithm Evaluation Conclusion Q&A 2

3. Outline Introduction P 2 Architecture Data Acquisition Algorithm Evaluation Conclusion Q&A 3

4. Introduction Motivation – Maintain roadways spend millions of dollars, but few feel comfortable. – There are cause of expensive lawsuits and claim. Keeping roadways in good condition. Inform drivers of hazardous road conditions. 4

5. Introduction (cont.) Goal – “Detecting and reporting the surface condition of roads”. 5

6. Introduction (cont.) Pothole Patrol (P 2 ) – Deploy P 2 on 7 taxis running in the Boston. – P 2 uses the inherent mobility of the participating vehicles. – Opportunistically gather data from accelerometer and GPS sensors. – Process the data by using machine learning to assess road surface condition. 6

7. Introduction (cont.) Accelerometer & GPS TAXI 7 7 cabs are able to cover 2492 distinct kilometers during their normal driving in 10 days

8. Outline Introduction P 2 Architecture Data Acquisition Algorithm Evaluation Conclusion Q&A 8

9. P 2 Architecture (cont.) Testbed : Soekris 4801 embedded computer(Linux) +WiFi card + Network card + GPS + 3-axis accelerometer Testbed : Soekris 4801 embedded computer(Linux) +WiFi card + Network card + GPS + 3-axis accelerometer Placement (Consider by accelerometer ) Firmly attached to dashboard inside car’s glove box Placement (Consider by accelerometer ) Firmly attached to dashboard inside car’s glove box 9 Soekris 4801

10. P 2 Architecture Accelerometer & GPS Sensing Location Interpolation FilteringNetwork uploadDatabase StoringClustering Client (Cars) Server 10

11. P 2 Architecture (cont.) 121°E31 25°N02 Sensing Combine & Filter Upload & Store Clustering Final Report 11

12. P 2 Architecture (cont.) 12

13. Outline Introduction P 2 Architecture Data Acquisition Algorithm Evaluation Conclusion Q&A 13

14. Data Acquisition The distribution of coverage density across all the lengths of road encountered. 14

15. Data Acquisition (cont.) Accelerometer Placement 15 Good

16. Data Acquisition (cont.) GPS Accuracy GPS accuracy in our deployment is important. Measure accuracy – Placed a thick metal bar across a road, and repeatedly drove over it. – standard deviation of the positions reported for the bar to be 3.3 meters. 16

17. Data Acquisition (cont.) Hand-labeled Training Data Collect the data by repeatedly driving down several known stretches of road in the Boston. Event Class 17

18. Data Acquisition (cont.) Loosely Labeled Training Data Hand-labeled less coverage non-pothole type With types and rough frequency of anomalies Without exact number and location Example 18

19. Outline Introduction P 2 Architecture Data Acquisition Algorithm Evaluation Conclusion Q&A 19

20. Algorithm Main Concept: Various road conditions introduce high z-axis acceleration. It is less sufficient to only use accelerometer to identify the real pothole! Use filter to reject non-pothole even type. 20

21. Filtering stage 21

22. txtx t x Z-peak XZ-ratio Speed vs Z ratio OUT : pothole detection IN :windows of all event type Minor anomalies Expansion joint,rail crossings Smaller highway anomalies tztz t z tsts t s Z<= t z Z<= t z X<= t x* Z Z<= t S* V Z<= t S* V 22 Filter

23. Tuning the parameter Goal : minimizing false positive rate Corr is the number of correct detections of pothole Incorr is the number of incorrect detection of pothole (false positive) t ={ t z, t x, t s } Arg max s(t) t Arg max s(t) t s(t)=corr-incorr^2 minimizing false positive rate 23

24. Outline Introduction P 2 Architecture Data Acquisition Algorithm Evaluation Conclusion Q&A 24

25. Performance of labeled data Classbeforeafter Pothole88.9%92.4% Manhole 0.3% Exp. Joint2.7%0.0% Railroad Crossing8.1%7.3% Test data of listed class that was reported as potholes by our algorithm, before and after training on additional loosely labeled data. False positive rate : 7.6% 25

26. Impact of features and thresholds Z-peark filter only Z-peak, xz ratio Z-peak, xz ratio, speed vs z ratio 26

27. Performance on uncontrolled cab data Using 7 taxis running through the Boston downtown to collect data Clustering the detections that requiring 4 trace before reporting them as a detection 27

28. 48 detections with cluster size 4 28

29. Outline Introduction P 2 Architecture Data Acquisition Algorithm Evaluation Conclusion Q&A 29

30. Conclusion Studied an application of mobile sensing: detecting and reporting the surface conditions of roads. By using GPS and accelerometer, the P^2 system is able to detect adverse road conditions. The false positive rate is very low in uncontrolled taxis experiment. 30

31. Q&A Thanks for listening 31