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DSDCS: Detection of Safe Driving via Crowd Sensing

  • Yun Du
  • Xin Guo
  • Chenyang Shi
  • Yifan Zhu
  • Bohan LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

Traffic safety plays an important role in smart transportation, and it has become a social issue worthy of attention. For detection of safe driving, we focus on the collection, processing, distribution, exchange, analysis and utilization of information, and aim at providing diverse services for drivers and passengers. By adopting crowdsourcing and crowd-sensing, we monitor the extreme driving behavior during the process of driving, trying to reduce the probability of traffic accidents. The smartphones are carried by passengers, which can sense the driving state of the vehicles with our proposed incentive mechanism. After the data is integrated, we are able to monitor the driving behavior more accurately, and finally secure the public transit. Finally, we developed a safe driving App for monitoring and evaluation.

Keywords

Crowd sensing Detection of safe driving Crowdsourcing incentive 

References

  1. 1.
    Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., ZeinalipourYazti, D.: Crowdsourcing with smartphones. IEEE Internet Comput. 16, 36–44 (2012)CrossRefGoogle Scholar
  2. 2.
    Guo, Y., Guo, B., Liu, Y., Wang, Z., Ouyang, Y., Yu, Z.: CrowdSafe: detecting extreme driving behaviors based on mobile crowdsensing. In: Proceedings of the 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2017), 4–8 August 2017, San Francisco, California, USA (2017)Google Scholar
  3. 3.
    Khaisongkram, W., Raksincharoensak, P., Shimosaka, M., Mori, T., Sato, T., Nagai, M.: Automobile driving behavior recognition using boosting sequential labeling method for adaptive driver assistance systems. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds.) KI 2008. LNCS (LNAI), vol. 5243, pp. 103–110. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-85845-4_13CrossRefGoogle Scholar
  4. 4.
    Ogie, R.I.: Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework. Hum.-Cent. Comput. Information Sci. 6(1), 24 (2016)CrossRefGoogle Scholar
  5. 5.
    Vasconcelos, I., Oliveira Vasconcelos, R., Olivieri, B., Roriz, M., Endler, M., Colaço Junior, M.: Smartphone-based outlier detection: a complex event processing approach for driving behavior detection. J. Internet Serv. Appl. 8(1) (2017)Google Scholar
  6. 6.
    Herzberg, P.Y.: Beyond “accident-proneness”: Using Five-Factor Model prototypes to predict driving behavior. J. Res. Pers. 43(6), 1096–1100 (2009)CrossRefGoogle Scholar
  7. 7.
    Toledo, T., Koutsopoulos, H.N., Ben-Akiva, M.: Integrated driving behavior modeling. Transp. Res. Part C: Emerg. Technol. 15(2), 96–112 (2007)CrossRefGoogle Scholar
  8. 8.
    Zhu, X., Yuan, Y., Hu, X., Chiu, Y.-C., Ma, Y.-L.: A Bayesian network model for contextual versus non-contextual driving behavior assessment. Transp. Res. Part C: Emerg. Technol. 81, 172–187 (2017)CrossRefGoogle Scholar
  9. 9.
    Liu, X., Ota, K., Liu, A., Chen, Z.: An incentive game based evolutionary model for crowd sensing networks. Peer-to-Peer Netw. Appl. 9(4), 692–711 (2016)CrossRefGoogle Scholar
  10. 10.
    Thejaswini, M., Rajalakshmi, P., Desai, U.B.: Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing. Peer-to-Peer Netw. Appl. 9(4), 721–730 (2016)CrossRefGoogle Scholar
  11. 11.
    Pournajaf, L., Xiong, L., Sunderam, V.: Dynamic data driven crowd sensing task assignment. Procedia Comput. Sci. 29 (2014)CrossRefGoogle Scholar
  12. 12.
    Li, H., Jia, K., Yang, H., Liu, D., Zhou, L.: Practical blacklist-based anonymous authentication scheme for mobile crowd sensing. Peer-to-Peer Netw. Appl. 9(4), 762–773 (2016)CrossRefGoogle Scholar
  13. 13.
    Guo, B., et al.: TaskMe: toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing. Int. J. Hum.-Comput. Stud. 102, 14–26 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yun Du
    • 1
  • Xin Guo
    • 1
  • Chenyang Shi
    • 1
  • Yifan Zhu
    • 1
  • Bohan Li
    • 1
    • 2
    Email author
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

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