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SmartRoad: Smartphone-Based Crowd Sensing for Traffic Regulator Detection and Identification

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Published:20 July 2015Publication History
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Abstract

In this article we present SmartRoad, a crowd-sourced road sensing system that detects and identifies traffic regulators, traffic lights, and stop signs, in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from in-vehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification under realistic and practical settings, SmartRoad automatically adapts to different application requirements by (i) intelligently choosing the most appropriate information representation and transmission schemes, and (ii) dynamically evolving its core detection and identification engines to effectively take advantage of any external ground truth information or manual label opportunity. We implemented SmartRoad on a vehicular smartphone test bed, and deployed it on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively, and efficiently carry out the detection and identification tasks.

References

  1. Apple iOS. 2014. http://www.apple.com/ios/.Google ScholarGoogle Scholar
  2. Audi Travolution. 2010. http://www.audiusanews.com/newsrelease.do?id=1812.Google ScholarGoogle Scholar
  3. J. Biagioni, T. Gerlich, T. Merrifield, and J. Eriksson. 2011. Easytracker: Automatic transit tracking, mapping, and arrival time prediction using smartphones. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys’11). 68--81. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. L. Breiman. 2001. Random forests. Machine Learning 45, 1 (2001), 5--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R. Carisi, E. Giordano, G. Pau, and M. Gerla. 2011. Enhancing in vehicle digital maps via GPS crowdsourcing. In WONS.Google ScholarGoogle Scholar
  6. Celery. 2014. http://celeryproject.org/.Google ScholarGoogle Scholar
  7. G. Chandrasekaran, T. Vu, A. Varshavsky, M. Gruteser, R. P. Martin, J. Yang, and Y. Chen. 2011. Tracking vehicular speed variations by warping mobile phone signal strengths. In PerCom. IEEE, 213--221. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. S. Dasgupta and J. Langford. 2009. A tutorial on active learning. International Conference on Machine Learning. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. de la Escalera, J. M. Armingol, and M. Mata. 2003. Traffic sign recognition and analysis for intelligent vehicles. Image and Vision Computing 21, 3 (2003), 247--258.Google ScholarGoogle ScholarCross RefCross Ref
  10. Django. 2014. https://www.djangoproject.com//.Google ScholarGoogle Scholar
  11. S. B. Eisenman, E. Miluzzo, N. D. Lane, R. A. Peterson, G. S. Ahn, and A. T. Campbell. 2009. BikeNet: A mobile sensing system for cyclist experience mapping. ACM Transactions on Sensor Networks (TOSN) 6, 1 (2009), 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Galaxy Nexus. 2013. Homepage. Retrieved from http://www.android.com/devices/detail/galaxy-nexus/.Google ScholarGoogle Scholar
  13. R. K. Ganti, N. Pham, H. Ahmadi, S. Nangia, and T. F. Abdelzaher. 2010. GreenGPS: A participatory sensing fuel-efficient maps application. In MobiSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Garmin. 2013. Garmin: Choose Your Country. Retrieved from http://www8.garmin.com/buzz/ecoroute/.Google ScholarGoogle Scholar
  15. Google Android. 2013. Homepage. Retrieved from http://www.android.com/.Google ScholarGoogle Scholar
  16. Google Maps. 2014. Homepage. Retrieved from https://www.google.com/maps/preview/.Google ScholarGoogle Scholar
  17. Google. Google Self-Driving Car Project. 2015. http://www.google.com/selfdrivingcar/.Google ScholarGoogle Scholar
  18. J. Han, M. Kamber, and J. Pei. 2011. Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. T. Hao, G. Xing, and G. Zhou. 2013. iSleep: Unobtrusive sleep quality monitoring using smartphones. In SenSys. ACM, 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. S. Hemminki, P. Nurmi, and S. Tarkoma. 2013. Accelerometer-based transportation mode detection on smartphones. In SenSys. ACM, 13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. S. Hu, H. Liu, L. Su, H. Wang, T. F. Abdelzaher, P. Hui, W. Zheng, Z. Xie, and J. Stankovic. 2014. Towards automatic phone-to-phone communication for vehicular networking applications. In INFOCOM.Google ScholarGoogle Scholar
  22. S. Hu, L. Su, S. Li, S. Wang, C. Pan, S. Gu, T. Amin, H. Liu, S. Nath, R. R. Choudhury, and T. F. Abdelzaher. 2015. Experiences with eNav: A Low-power vehicular navigation system. In UbiComp.Google ScholarGoogle Scholar
  23. B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden. 2006. CarTel: A distributed mobile sensor computing system. In SenSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. E. Koukoumidis, L. S. Peh, and M. R. Martonosi. 2011. SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory. In MobiSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. A. Krause and C. Guestrin. 2007. Nonmyopic active learning of Gaussian processes: An exploration-exploitation approach. In ICML. ACM, 449--456. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. S. Maldonado-Bascon, S. Lafuente-Arroyo, P. Gil-Jimenez, H. Gomez-Moreno, and F. López-Ferreras. 2007. Road-sign detection and recognition based on support vector machines. IEEE Transcations on Intelligent Transportation Systems 8, 2 (2007), 264--278. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. E. Miluzzo, C. T. Cornelius, A. Ramaswamy, T. Choudhury, Z. Liu, and A. T. Campbell. 2010. Darwin phones: The evolution of sensing and inference on mobile phones. In MobiSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. T. M. Mitchell. 1997. Machine Learning. McGraw-Hill. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West, and P. Boda. 2009. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In MobiSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. S. Nath. 2012. ACE: Exploiting correlation for energy-efficient and continuous context sensing. In MobiSys. ACM, 29--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. Nawaz, C. Efstratiou, and C. Mascolo. 2013. ParkSense: A smartphone based sensing system for on-street parking. In MobiCom. ACM, 75--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. A. Y. Ng, M. I. Jordan, Y. Weiss, and others. 2002. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 2, 849--856.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Open Street Map. 2014. http://www.openstreetmap.org/.Google ScholarGoogle Scholar
  34. J. Qiu, D. Chu, X. Meng, and T. Moscibroda. 2011. On the feasibility of real-time phone-to-phone 3D localization. In SenSys. ACM, 190--203. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. R. K. Rana, C. T. Chou, S. S. Kanhere, N. Bulusu, and W. Hu. 2010. Ear-phone: An end-to-end participatory urban noise mapping system. In IPSN. ACM, 105--116. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. F. Saremi, O. Fatemieh, H. Ahmadi, H. Wang, T. Abdelzaher, R. Ganti, H. Liu, S. Hu, S. Li, and L. Su. 2015. Experiences with greengps—fuel-efficient navigation using participatory sensing. IEEE Transactions on Mobile Computing (TMC).Google ScholarGoogle Scholar
  37. A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, and J. Eriksson. 2009. VTrack: Accurate, energy-aware road traffic delay estimation using mobile phones. In SenSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. A. Thiagarajan, L. S. Ravindranath, H. Balakrishnan, S. Madden, and L. Girod. 2011. Accurate, low-energy trajectory mapping for mobile devices. In NSDI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. D. Wang, T. Abdelzaher, L. Kaplan, R. Ganti, S. Hu, and H. Liu. 2013a. Exploitation of physical constraints for reliable social sensing. In RTSS. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. H. Wang, X. Bao, R. R. Choudhury, and S. Nelakuditi. 2013b. InSight: Recognizing humans without face recognition. In HotMobile. ACM, 7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Y. Wang, X. Liu, H. Wei, G. Forman, C. Chen, and Y. Zhu. 2013c. CrowdAtlas: Self-updating maps for cloud and personal use. In MobiSys. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Y. Wang, J. Yang, H. Liu, Y. Chen, M. Gruteser, and R. P. Martin. 2013d. Sensing vehicle dynamics for determining driver phone use. In Mobisys. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Waze. 2014. Waze Social GPS Maps Traffic. Retrieved from https://play.google.com/store/apps/details?id=com. waze.Google ScholarGoogle Scholar
  44. C.-W. You, N. D. Lane, F. Chen, R. Wang, Z. Chen, T. J. Bao, M. Montes-de Oca, Y. Cheng, M. Lin, L. Torresani, and A. Campbell. 2013. CarSafe App: Alerting drowsy and distracted drivers using dual cameras on smartphones. In MobiSys. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. X. Zhu. 2008. Semi-Supervised Learning Literature Survey. Computer Science Technical Report. University of Wisconsin-Madison.Google ScholarGoogle Scholar

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          • Published in

            cover image ACM Transactions on Sensor Networks
            ACM Transactions on Sensor Networks  Volume 11, Issue 4
            December 2015
            368 pages
            ISSN:1550-4859
            EISSN:1550-4867
            DOI:10.1145/2782756
            • Editor:
            • Chenyang Lu
            Issue’s Table of Contents

            Copyright © 2015 ACM

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            New York, NY, United States

            Publication History

            • Published: 20 July 2015
            • Accepted: 1 April 2015
            • Revised: 1 October 2014
            • Received: 1 December 2013
            Published in tosn Volume 11, Issue 4

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