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Smartphone-Based Intelligent Driver Assistant: Context Model and Dangerous State Recognition Scheme

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

The paper proposes the context model and the dangerous state recognition scheme for intelligent driver assistant system. The system is aimed at utilization of smartphone’s front-facing camera and other sensors for dangerous states reignition to prevent emergency and reduce the accidents probability. The proposed context model is divided into following types of contexts, driver context, vehicle context, road context, and environment context. The model shows how the smartphone front-facing camera and sensors as soon as accessible Internet services are used to support the proposed context types. Then, the context-based dangerous state recognition model is presented. The model calculates the computational power of the smartphone and based on this information implements the frame skipping algorithm to reduce the computation complexity for the smartphone processor. The implementation of the proposed frame skipping model shows that for the modern smartphones the complexity is decreased by three times in compare with usual scheme. The proposed context model and dangerous state recognition scheme has been implemented in Drive Safely system that is available in Google Play.

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Notes

  1. 1.

    https://play.google.com/store/apps/details?id=ru.igla.drivesafely.

  2. 2.

    https://kotlinlang.org/.

References

  1. Dey, A.K.: Understanding and using context. Pers. Ubiquitous Comput. 5(1), 4–7 (2001)

    Article  Google Scholar 

  2. Smirnov, A., Kashevnik, A., Lashkov, I., Hashimoto, N., Boyali, A.: Smartphone-based two-wheeled self-balancing vehicles rider assistant. In: Proceedings of the 17th IEEE Conference of the Open Innovations Association FRUCT, Yaroslavl, Russia, pp. 201–209 (2015)

    Google Scholar 

  3. Smirnov, A., Kashevnik, A., Lashkov, I., Baraniuc, O., Parfenov, V.: Smartphone-based dangerous situation identification while driving: algorithms and implementation. In: Proceedings of the 18th IEEE Conference of the Open Innovations Association FRUCT, Finland, pp. 306–313 (2016)

    Google Scholar 

  4. Lashkov, I., Smirnov, A., Kashevnik, A., Parfenov, V.: Ontology-based approach and implementation of ADAS system for mobile device use while driving. In: Proceedings of the 6th International Conference on Knowledge Engineering and Semantic Web, Moscow, CCIS 518, pp. 117–131 (2015)

    Google Scholar 

  5. Fazeen, M., Gozick, B., Dantu, R., Bhukhiya, M., Gonzalez, M.C.: Safe driving using mobile phones. IEEE Trans. Intell. Transp. Syst. 13(3), 1462–1468 (2012)

    Article  Google Scholar 

  6. Dumitru, A.I., Girbacia, T., Boboc, R.G., Postelnicu, C.-C., Mogan, G.-L.: Effects of smartphone based advanced driver assistance system on distracted driving behavior: a simulator study. Comput. Hum. Behav. 83, 1–7 (2018)

    Article  Google Scholar 

  7. Eftekhari, H.R., Ghatee, M.: A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors. J. Intell. Transp. Syst. 23, 1–12 (2019)

    Article  Google Scholar 

  8. Pandey, P.S.K., Kulkarni, R.: Traffic sign detection for advanced driver assistance system. In: 2018 International Conference on Advances in Communication and Computing Technology (ICACCT), Sangamner, pp. 182–185 (2018)

    Google Scholar 

  9. Yun, D.S., Lee, J., Lee, S., Kwon, O.: Development of the eco-driving and safe-driving components using vehicle information. In: 2012 International Conference on ICT Convergence (ICTC), Jeju Island, pp. 561–562 (2012)

    Google Scholar 

  10. Hirschfeld, R.A.: Integration of Vehicle On-Board Diagnostics and Smart Phone Sensors. US20110012720A1, United States Patent and Trademark Office, 20 January 2011

    Google Scholar 

  11. Fang, A., Qiu, C., Zhao, L., Jin, Y.: Driver risk assessment using traffic violation and accident data by machine learning approaches. In: 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE), Singapore, pp. 291–295 (2018)

    Google Scholar 

  12. Suzuki, H., Marumo, Y.: A new approach to green light optimal speed advisory (GLOSA) systems and its limitations in traffic flows. In: Proceedings of the 1st International Conference on Human Systems Engineering and Design (IHSED2018): Future Trends and Applications, 25–27 October 2018. CHU-Université de Reims Champagne-Ardenne, France, pp. 776–782 (2019)

    Google Scholar 

  13. Biswas, S., Tatchikou, R., Dion, F.: Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety. IEEE Commun. Mag. 44(1), 74–82 (2006)

    Article  Google Scholar 

  14. Abdulsattar, H., Mostafizi, A., Siam, M.R.K., Wang, H.: Measuring the impacts of connected vehicles on travel time reliability in a work zone environment: an agent-based approach. In: Journal of Intelligent Transportation Systems Technology Planning and Operations (2019)

    Google Scholar 

  15. Kulla, E., Jiang, N., Spaho, E., Nishihara, N.: A survey on platooning techniques in VANETs. In: Complex, Intelligent, and Software Intensive Systems, pp. 650–659. Springer (2019)

    Google Scholar 

  16. Mahmood, A., Butler, B., Sheng, Q.Z., Zhang, W.E., Jennings, B.: Need of ambient intelligence for next-generation connected and autonomous vehicles. In: Guide to Ambient Intelligence in the IoT Environment, pp. 133–151. Springer (2019)

    Google Scholar 

  17. Aziz, Z., Nandi, A. Microsoft Technology Licensing LLC. Digital assistant for vehicle related activities. US10169794B2, United States Patent and Trademark Office, 01 January 2019

    Google Scholar 

  18. Wolverton, M.J., Mark, W.S., Bratt, H., Bercow, D.A.: SRI International. Vehicle personal assistant. US20140136187A1, United States Patent and Trademark Office, 19 January 2012

    Google Scholar 

  19. Engelbrecht, J., Booysen, M.J.(Thinus), van Rooyen, G.-J., Bruwer, F.J.: A survey of smartphone-based sensing in vehicles for ITS applications. IET Intell. Transp. Syst. 9, 1–22 (2015)

    Google Scholar 

  20. Singh, G., Bansal, D., Sofat, S.: A smartphone-based technique to monitor driving behavior using DTW and crowdsensing. Pervasive Mob. Comput. 40(9), 56–70 (2017)

    Article  Google Scholar 

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Acknowledgments

The research is funded by the Russian Science Foundation (project # 18-71-10065).

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Authors and Affiliations

  1. SPIIRAS, 39, 14 Line, Saint Petersburg, 199178, Russia

    Igor Lashkov & Alexey Kashevnik

  2. ITMO University, Kronverkskiy Prospekt, 49, Saint Petersburg, 197101, Russia

    Igor Lashkov & Alexey Kashevnik

Authors
  1. Igor Lashkov
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  2. Alexey Kashevnik
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Corresponding author

Correspondence to Igor Lashkov .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Lashkov, I., Kashevnik, A. (2020). Smartphone-Based Intelligent Driver Assistant: Context Model and Dangerous State Recognition Scheme. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_11

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