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Study of Mechanized Recognition of Driver’s Smartphone Exploiting Common Vehicle-Riding Actions

  • Kadiyala YaswanthEmail author
  • Rajasekhar Manda
  • Durgesh Nandan
Conference paper
  • 36 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1162)

Abstract

体育赛事投注记录distracted driving due to using smartphone like texting, browsing web, etc. increases the risk of accidents. to prevent this distracted driving, many suggestions have been proposed, but out of them, none addressed completely and efficiently to prevent this distracted driving. this work presents a concept called as mechanized recognition of driver’s smart phone exploiting common vehicle-riding actions to overcome above said deficiency concept. the fusion of the driver’s smartphone with phone’s sensory provides the information related to rider’s actions. this information can be obtained by sequence of steps such as walking toward the vehicle, opening the door from driver’s side, closing the door, standing near the vehicle, entering into it, sitting, and kicking of the engine. the recognition of the smartphone depends on the position of the smartphone placed in the vehicle. this concept identifies the driver’s smartphone just before it leaves out of the parked location. it differentiates between the seated rows by detecting the electromagnetic (em) spikes occurring when the vehicle starts. by conducting all these sequences of steps, this concept will effectively identify the driver’s smartphone and which efficiently prevent distracted driving.

Keywords

Detector Automatic identification of driver’s smartphone EMF fluctuations Electronic devices Motorizes Sensors Entrance detector Vehicle door closing sound 

References

  1. 1.
    Ajay Kumar Reddy, G.S., Jagadesh Chandra, S.V., Naresh Kumar Reddy, B.: Developing the fabricated system of automatic vehicle identification using RFID based poultry traceability system. In: International Conference on Information Communication and Embedded Systems, pp. 1–6 (2014)
  2. 2.
    Su, W., Hu, K., Zhang, L., Ma, L.: A RFID based material supply management system in automatic vehicle assembly streamline. In: Information Technology and Computer Science (ITCS 2009), pp. 259–262. IEEE Press (2009)
  3. 3.
    Tashk, A., Helfroush, M., Karimi, V.: An automatic traffic control system based on simultaneous Persian license plate recognition and driver fingerprint identification. In: Telecommunications Forum (TELFOR), pp. 1729–1732 (2012)
  4. 4.
    Hong, T., Qin, H., Sun, Q.: An improved real time eye state identification system in driver drowsiness detection. IEEE In: International Conference on Control and Automation (ICCA2007), Guangzhou, CHINA, May 30–June 1 (2007)
  5. 5.
    Burnham, G.O., Seo, J., Bekey, G.A.: Identification of human driver models in car following. IEEE Trans. Autom. Cont. 19, 911–915 (1974)
  6. 6.
    Sonnerat, D., Tricot, N., Popieul, J.: Driver’s environment identification using automatic classification methods. In: Active Safety Application Intelligent Vehicle Symposium. IEEE (2002)
  7. 7.
    Kaplan, K., Kurtul, C., Akin, H.L.: ADES: automatic driver evaluation system. In: 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp. 442–447 (2012)
  8. 8.
    Krasner, G., Katz, E.: Automatic parking identification and vehicle guidance with road awareness. In: IEEE International Conference on the Science of Electrical Engineering (ICSEE)
  9. 9.
    Mahmood, Z., Ali, T., Khattak, S., Khan, S.U., Yang, L.T.: Automatic vehicle detection and driver identification framework for secure vehicle parking. In: 13th International Conference on Frontiers of Information Technology (FIT), pp. 6–11 (2015)
  10. 10.
    Martínez, M., Echanobe, J., del Campo, I.: Driver identification and impostor detection based on driving behavior signals. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 372–378 (2016)
  11. 11.
    Miyajima, C., Nishiwaki, Y., Ozawa, K., et al.: Driver modeling based on driving behavior and its evaluation in driver identification. Proc. IEEE 95(2), 427–437 (2007)
  12. 12.
    Park, H., Ahn, D.H., Park, T., Shin, K.G.: Automatic identification of driver’s smartphone exploiting common vehicle-riding actions. IEEE Trans. Mob. Comput. 17(2), 265–278 (2017)
  13. 13.
    Park, J.-g., Patel, A., Curtis, D., Teller, S., Ledlie, J.: Online pose classification and walking speed estimation using handheld devices. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 113–122. ACM (2012)
  14. 14.
    Electromagnetic capability:
  15. 15.
    Madgwick, S.O., Harrison, A.J., Vaidyanathan, R.: Estimation of imu and marg orientation using a gradient descent algorithm. In: 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–7. IEEE (2011)
  16. 16.
    Park, T., Shin, K.G.: Attack-tolerant localization via iterative verification of locations in sensor networks. ACM Trans. Embed. Comput. Syst. (TECS) 8(1), 2 (2008)
  17. 17.
    Cho, D.-K., Mun, M., Lee, U., Kaiser, W.J., Gerla, M.: Autogait: a mobile platform that accurately estimates the distance walked. In: 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 116–124. IEEE (2010)
  18. 18.
    Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. In: Pervasive Computing, pp. 1–16. Springer, Berlin (2006)
  19. 19.
    Ahn, S., Kim, J.: Magnetic field design for high efficient and low emf wireless power transfer in on-line electric vehicle. In: Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP), pp. 3979–3982. IEEE (2011)
  20. 20.
    U.S. HEV sales by Model (1999–2013):
  21. 21.
    Tarkoma, S., Siekkinen, M., Lagerspetz, E., Xiao, Y.: Smartphone Energy Consumption: Modeling and Optimization. Cambridge University Press, Cambridge (2014)
  22. 22.
    Mostofi, N., Elhabiby, M., El-Sheimy, N.: Indoor localization and mapping using camera and inertial measurement unit (imu). In: Position, Location and Navigation Symposium-PLANS 2014, 2014 IEEE/ION, pp. 1329–1335. IEEE (2014)
  23. 23.
    Shoemake, K.: Animating rotation with quaternion curves. ACM SIGGRAPH Comput. Graph. 19(3), 245–254 (1985)
  24. 24.
    Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Crystallogr. Sect. A Cryst. Phys. Diff. Theor. Gen. Crystallogr. 32(5), 922–923 (1976)
  25. 25.
    Bobick, N.: Rotating objects using quaternions. Game Dev. 2(26), 21–31 (1998)
  26. 26.
    Heise, R., MacDonald, B.A.: Quaternions and motion interpolation: a tutorial. In: New Advances in Computer Graphics, pp. 229–243. Springer, Berlin (1989)
  27. 27.
    Yazdi, A., Lin, D., Heydari, P.: A 1.8 V three-stage 25 GHz 3 dB-BW differential non-uniform downsized distributed amplifier. IEEE ISSCC Tech. Dig. 156–158 (2005)
  28. 28.
    Iyer, B., Pathak, N.P., Ghosh, D.: Dual-input dual-output RF sensor for indoor human occupancy and position monitoring. IEEE Sens. J. 15(7), 3959–3966 (2015)
  29. 29.
    Iyer, B., Kumar, A., Pathak, N.P., Ghosh, D.: Concurrent multi-band RF system for search and rescue of human life during natural calamities. In: IEEE MTT-S International Microwave and RF Conference, pp. 1–4 (2013)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Kadiyala Yaswanth
    • 1
    Email author
  • Rajasekhar Manda
    • 1
  • Durgesh Nandan
    • 2
  1. 1.Department of ECEAditya Engineering CollegeSurampalemIndia
  2. 2.Accendere Knowledge Management Services Pvt. Ltd., CL Educate Ltd.New DelhiIndia

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