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Improving Social Safety with Automobile Pilot Adroitness Analyzer

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Smart Intelligent Computing and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 104))

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Abstract

The number of losses and fatal accidents in developing countries, where traffic rules are not paid much attention to, due to the recklessness of drivers is far too great. Thus, a thorough analysis of driving behavior and driving habits should be performed before issuing a driving license to any driver. The current license issue process involves manual checking of the proficiency of a driver. This kind of process has several loopholes. To avoid such shortcomings, a low-cost system is developed, which digitally monitors a driver’s habits. It considers some very important and common metrics that a driver is expected to follow, before qualifying for one’s proficiency. A classification is made based on all of the logged data, using various data analysis algorithms. The same can be used to determine whether a driver is fit to drive or not.

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References

  1. Opila, D.F., Aswani, D., McGee, R., Cook, J.A., Grizzle, J.W.: Incorporating drivability metrics into optimal energy management strategies for hybrid vehicles. In: 47th IEEE Conference on decision and Control, December 9, pp. 4382–4389 (2008)

    Google Scholar 

  2. Liu, Y., Wu, Z.: Multitasking driver cognitive behavior modeling, In: 3rd IEEE International Conference on Intelligent Systems, pp. 52–57 (2006)

    Google Scholar 

  3. Remboski, D., Douros, K., Lee, J., Gardner, J.L., Gardner, R.M., Hurwitz, J.B., Leivian, R.H., Nagel, J., Wheatley, D.J., Wood, C.A.: Method and apparatus for vehicle operator performance assessment and improvement, United States patents US 6,925,425. August 2 (2005)

    Google Scholar 

  4. Harkness, R.: Advanced Drivers Education Products, Training, Driver training system, United States patent US 6,227,862. May 8 (2001)

    Google Scholar 

  5. Burch, L.A.: Driving simulator and method of evaluation of driver competency, United States patent application US 11/903,152. September 20 (2007)

    Google Scholar 

  6. Kumar, A., Mudhole, S.S., Lemoine, B.: A smart sensor-based software system for driver evaluation, In 4th Annual Systems Conference, IEEE, April, pp. 472–477 (2010)

    Google Scholar 

  7. Clement, F.S.C., Vashistha, A., Rane, M.E.: Driver fatigue detection system, In International Conference on Information Processing (ICIP), December, pp. 229–234 (2015)

    Google Scholar 

  8. Bezet, O., Cherfaoui, V., Bonnifait, P.: A system for driver behavioral indicators processing and archiving. In: Intelligent Transportation Systems Conference, ITSC’06. IEEE September 17, pp. 799–804 (2006)

    Google Scholar 

  9. Mandal, B., Li, L., Wang, G.S., Lin, J.: Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans. Intell. Transp. Syst. (2017)

    Google Scholar 

  10. Tang, X., Zhou, P., Wang, P.: Real-time image-based driver fatigue detection and monitoring system for monitoring driver vigilance. In: 35th Chinese Control Conference (CCC), July, pp. 4188–4193 (2016)

    Google Scholar 

  11. Wongphanngam, J., Pumrin, S.: Fatigue warning system for driver nodding off using depth image from Kinect. In: 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), June, pp. 1–6 (2016)

    Google Scholar 

  12. He, Q., Li, W., Fan, X., Fei, Z.: Evaluation of driver fatigue with multi-indicators based on Artificial Neural Network. IET Intell. Transp. Syst. (2016)

    Google Scholar 

  13. Toma, M.I., Rothkrantz, L.J., Antonya, C.: Car driver skills assessment based on driving postures recognition. In: 3rd International Conference on Cognitive Infocommunications (CogInfoCom), IEEE, December, pp. 439–446 (2012)

    Google Scholar 

  14. Osgouei, R.H., Choi, S.: Evaluation of driving skills using an HMM-based distance measure. In: International Workshop on Haptic Audio Visual Environments and Games (HAVE), IEEE, October, pp. 50–55 (2012)

    Google Scholar 

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Correspondence to Bhupesh Singh .

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Singh, B., Balasubramanian, P., Mathew, J., John, B. (2019). Improving Social Safety with Automobile Pilot Adroitness Analyzer. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_9

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  • DOI: https://doi.org/10.1007/978-981-13-1921-1_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1920-4

  • Online ISBN: 978-981-13-1921-1

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