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.
- Apple iOS. 2014. http://www.apple.com/ios/.Google Scholar
- Audi Travolution. 2010. http://www.audiusanews.com/newsrelease.do?id=1812.Google Scholar
- 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 ScholarDigital Library
- L. Breiman. 2001. Random forests. Machine Learning 45, 1 (2001), 5--32. Google ScholarDigital Library
- R. Carisi, E. Giordano, G. Pau, and M. Gerla. 2011. Enhancing in vehicle digital maps via GPS crowdsourcing. In WONS.Google Scholar
- Celery. 2014. http://celeryproject.org/.Google Scholar
- 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 ScholarDigital Library
- S. Dasgupta and J. Langford. 2009. A tutorial on active learning. International Conference on Machine Learning. Google ScholarDigital Library
- 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 ScholarCross Ref
- Django. 2014. https://www.djangoproject.com//.Google Scholar
- 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 ScholarDigital Library
- Galaxy Nexus. 2013. Homepage. Retrieved from http://www.android.com/devices/detail/galaxy-nexus/.Google Scholar
- 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 ScholarDigital Library
- Garmin. 2013. Garmin: Choose Your Country. Retrieved from http://www8.garmin.com/buzz/ecoroute/.Google Scholar
- Google Android. 2013. Homepage. Retrieved from http://www.android.com/.Google Scholar
- Google Maps. 2014. Homepage. Retrieved from https://www.google.com/maps/preview/.Google Scholar
- Google. Google Self-Driving Car Project. 2015. http://www.google.com/selfdrivingcar/.Google Scholar
- J. Han, M. Kamber, and J. Pei. 2011. Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. Google ScholarDigital Library
- T. Hao, G. Xing, and G. Zhou. 2013. iSleep: Unobtrusive sleep quality monitoring using smartphones. In SenSys. ACM, 4. Google ScholarDigital Library
- S. Hemminki, P. Nurmi, and S. Tarkoma. 2013. Accelerometer-based transportation mode detection on smartphones. In SenSys. ACM, 13. Google ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- E. Koukoumidis, L. S. Peh, and M. R. Martonosi. 2011. SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory. In MobiSys. Google ScholarDigital Library
- A. Krause and C. Guestrin. 2007. Nonmyopic active learning of Gaussian processes: An exploration-exploitation approach. In ICML. ACM, 449--456. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- T. M. Mitchell. 1997. Machine Learning. McGraw-Hill. Google ScholarDigital Library
- 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 ScholarDigital Library
- S. Nath. 2012. ACE: Exploiting correlation for energy-efficient and continuous context sensing. In MobiSys. ACM, 29--42. Google ScholarDigital Library
- S. Nawaz, C. Efstratiou, and C. Mascolo. 2013. ParkSense: A smartphone based sensing system for on-street parking. In MobiCom. ACM, 75--86. Google ScholarDigital Library
- 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 ScholarDigital Library
- Open Street Map. 2014. http://www.openstreetmap.org/.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- A. Thiagarajan, L. S. Ravindranath, H. Balakrishnan, S. Madden, and L. Girod. 2011. Accurate, low-energy trajectory mapping for mobile devices. In NSDI. Google ScholarDigital Library
- 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 ScholarDigital Library
- H. Wang, X. Bao, R. R. Choudhury, and S. Nelakuditi. 2013b. InSight: Recognizing humans without face recognition. In HotMobile. ACM, 7. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Waze. 2014. Waze Social GPS Maps Traffic. Retrieved from https://play.google.com/store/apps/details?id=com. waze.Google Scholar
- 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 ScholarDigital Library
- X. Zhu. 2008. Semi-Supervised Learning Literature Survey. Computer Science Technical Report. University of Wisconsin-Madison.Google Scholar
Index Terms
- SmartRoad: Smartphone-Based Crowd Sensing for Traffic Regulator Detection and Identification
Recommendations
Poster abstract: SmartRoad: a crowd-sourced traffic regulator detection and identification system
IPSN '13: Proceedings of the 12th international conference on Information processing in sensor networksIn this paper we present SmartRoad, a crowd-sourced 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 ...
SmartRoad: A New Approach to Law Enforcement in Dense Traffic Environments
ITSC '15: Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation SystemsRoad transport law enforcement is facing new challenges against the background of increasingly congested traffic conditions. An ideal law enforcement system must enable authorities to detect, identify and act against illegal road users with minimum ...
A Mobile Crowd Sensing Framework for Toll Plaza Delay Optimization
WCI '15: Proceedings of the Third International Symposium on Women in Computing and InformaticsThis paper presents a novel approach to solve the problem of reducing delays in toll plazas. Causes of delay in manual toll plazas are lane selected by the driver, time taken to pay the toll and efficiency of toll collectors. The proposed system uses ...
Comments