An IoT Enabled Vehicular Decision Fusion Framework for Accident Detection and Classification

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Published Jul 1, 2020
https://doi.org/10.47164/ijngc.v11i2.176
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Volume 11, Issue 2, July 2020

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Nikhil Kumar
Department of Computer Science and Engineering, School of Engineering, Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh, India - 201314
Debopam Acharya
School of Computing, DIT University, Dehradun, Uttarakhand, India - 248009
DIVYA LOHANI
Department of Computer Science and Engineering, School of Engineering, Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh, India - 201314

Abstract

Increased number of vehicle-based road accidents is a key reason for the death and disability of people. Timely information on accidents can save lives. Current accident detection systems are either working towards increasing the accuracy of detection or the severity of the accident. Accurate information of an accident type can help the emergency medical services (EMS) to identify the most appropriate type of rescue and medical assistance to the victims. This work introduces a smartphone-based accident detection and classification (ADC) system that not only detects the accident but also classifies the type of accident as collision, rollover, or fall-off, using internal and external sensors. We have developed an end-to-end IoT system that exploits a multi-sensor data fusion framework to accurately classify the type of accident. The framework combines the decisions of three different classifiers based on Nave Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF) methods using stacking ensemble approach. Logistic Regression based stacking approach is found to be highly accurate in comparison to NB, KNN, and RF classifiers when they were used individually.

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How to Cite
Nikhil Kumar, Debopam Acharya, & DIVYA LOHANI. (2020). An IoT Enabled Vehicular Decision Fusion Framework for Accident Detection and Classification. International Journal of Next-Generation Computing, 11(2), 191–211. https://doi.org/10.47164/ijngc.v11i2.176
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References

  1. Acharya, D., Kumar, V., Gaddis, G. M. 2007. A Mobile System for Detecting and Notifying Vehicle Rollover Events. 15th International Conference on Advanced Computing and Communications (ADCOM 2007), Guwahati, Assam. pp. 268-275.
  2. Adeniyi, D.A., Wei, Z., Yongquan, Y. 2016, Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics, Vol. 12, Issue 1, Pp. 90-108.
  3. Aloul, F., Zualkernan, I., Abu-Salma, R., Al-Ali, H., Al-Merri, M. 2015. iBump: Smartphone application to detect car accidents. Computers & Electrical Engineering. vol. 43, Pages 66-75.
  4. Barabba, V., Huber, C., Cooke, F., Pudar, N., Smith, J., and Paich, M. 2002. A Multimethod Approach for Creating New Business Models: The General Motors OnStar Project. INFORMS Journal on Applied Analytics 32:1, 20-34
  5. Bhatti, F., Shah, M.A., Maple, C., Islam, S.U. 2019. A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments. Sensors (Basel). vol. 19(9): 2071.
  6. Biau, G. 2012. Analysis of a random forests model. J. Mach. Learn. Res. vol. 13, pp. 1063–1095.
  7. Breiman, L. 1996. Bagging Predictors. Machine Learning. 24:2, pp. 123—140
  8. Breiman, L. 1996. Stacked Regressions, Machine Learning. 24: 1, pp. 49—64
  9. Breiman, L. 2001. Random forests. Mach. Learn. vol. 45, no. 1, pp. 5–32.
  10. Breiman, L., Friedman, J. H., Olshen, R., and Stone, C. J. 1984. Classification and Regression Trees. Belmont, CA, USA: Wadsworth.
  11. Büchlmann, P., Yu, B. 2002. Analyzing bagging, Annals of Statistics. 30, pp. 927—961
  12. Chan C.Y. 2002. A Treatise on Crash Sensing for Automotive Air Bag Systems. IEEE/ASME Transactions on Mechatronics. 7(2), pp.220-234.
  13. Chung, Y., Recker, W. W. 2012. A Methodological Approach for Estimating Temporal and Spatial Extent of Delays Caused by Freeway Accidents. IEEE Transactions on Intelligent Transportation Systems. vol. 13, no. 3, pp. 1454-1461.
  14. Damousis, G., Tzovaras, D. 2008. Fuzzy Fusion of Eyelid Activity Indicators for Hypovigilance-Related Accident Prediction. IEEE Transactions on Intelligent Transportation Systems. vol. 9, no. 3, pp. 491-500.
  15. Dar, B. K., Shah, M. A., Islam, S. U., Maple, C., Mussadiq, S., Khan, S. 2019. Delay-Aware Accident Detection and Response System Using Fog Computing. IEEE Access, vol. 7, pp. 70975-70985.
  16. Dietterich, T.G. 2000. Ensemble Methods in Machine Learning. In International Workshop on Multiple Classifier Systems. Springer. London, UK. pp. 1–15.
  17. Dunwoody, A. B., and Stern, D. S. 1998. System and method for the detection of vehicle rollover conditions. U.S. Patent 5 825 284.
  18. Elmenreich, W. 2002. An Introduction to Sensor Fusion - Research Report. Vienna University of Technology, Austria.
  19. Englisch, A. 2002. BMW ASSIST - TELEMATICS FOR SAFETY AND CONVENIENCE. 9th World Congress on Intelligent Transport Systems. Chicago, Illinois.
  20. Felisberto, F., Fdez.-Riverola, F., Pereira, A. 2014. A Ubiquitous and Low-Cost Solution for Movement Monitoring and Accident Detection Based on Sensor Fusion. Sensors. vol. 14, no. 5, pp. 8961–8983.
  21. Fernandes, B., Alam, M., Gomes, V., Ferreira, J., Oliveira, A. 2016. Automatic accident detection with multi-modal alert system implementation for ITS. Vehicular Communications. vol. 3, Pages 1-11.
  22. FIREBASE 2020. Google Firebase. Retrieved February 05, 2020 from https://firebase.google.com
  23. Freund, Y., Schapire, R.E. 1997. Decision-theoretic generalization of on-line learning and an application to boosting, J. Computer and System Sciences 55:1, pp. 119—139
  24. Genuer, R., Poggi, J.-M., and Tuleau, C. 2008. Random forests: Some methodological insights. INRIA. Saclay, France, Res. Rep. RR-6729.
  25. GSRRS. 2018. Global status report on road safety 2018: summary. Geneva: World Health Organization; 2018 (WHO/NMH/NVI/18.20). Licence: CC BY-NC-SA 3.0 IGO). Retrieved February 05, 2020 from https://www.who.int/violence_injury_prevention/road_safety_status/2018/English-Summary-GSRRS2018.pdf.
  26. Han, H., Guo, X., and Yu, H. 2016. Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest. 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). Beijing pp. 219-224.
  27. Ibrahim, H. A., Aly, A. K., Far, B. H. 2016. A system for vehicle collision and rollover detection. 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). Vancouver, BC. pp. 1-6.
  28. Iyoda, M., Trisdale, T., Sherony, R., Mikat, D., Rose, W. 2016. Event Data Recorder (EDR) Developed by Toyota Motor Corporation. SAE International Journal of Transportation Safety. Vol. 4, No. 1, pp. 187-201.
  29. Kendall, J., Solomon, K.A. 2014. Air bag deployment criteria, The Forensic Examiner (2014).
  30. Kubelka, V., Reinstein, M. 2012. Complementary filtering approach to orientation estimation using inertial sensors only. 2012 IEEE International Conference on Robotics and Automation. Saint Paul, MN. pp. 599-605.
  31. Kumar, N., Barthwal, A., Acharya, D., and Lohani, D. 2020. Modeling Vehicle Fall Detection Event using Internet of Things. In: S. M. Thampi et al. (Eds.): SIRS 2019, CCIS 1209. Springer Nature Singapore, pp. 1–14.
  32. Kumar, N., Barthwal, A., and Acharya, D. 2019. Modeling Vehicle Collision Events using Internet of Things. 2019 IEEE 16th India Council International Conference (INDICON). Rajkot, India. pp. 1-4.
  33. Kumar, N., Barthwal, A., Lohani, D. and Acharya, D. 2020. Vehicle Fall Severity Modeling using IoT and K-Nearest Neighbor Algorithm. 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS). Bengaluru, India. 2020, pp. 105-109.
  34. Kumar, N., Barthwal, A., Lohani, D., and Acharya, D. 2020. Modeling IoT Enabled Automotive System for Accident Detection and Classification. 2020 IEEE Sensors Applications Symposium (SAS). Kuala Lumpur, Malaysia.
  35. Laan, van der, M. J., Polley, E. C., and Hubbard. A. E. 2003. Super Learner. Statistical Applications in Genetics and Molecular Biology 6 (1).
  36. Langheim, J. 2002. Environment Sensing for Advanced Driver Assistance — CARSENSE. In: Krueger S., Gessner W. (eds) Advanced Microsystems for Automotive Applications Yearbook. VDI-Buch. Springer, Berlin, Heidelberg
  37. Lee, Y., Yeh, H., Kim, K. -H., and Choi, O. 2018. A real-time fall detection system based on the acceleration sensor of smartphone. International Journal of Engineering Business Management.
  38. Lerner, E.B. and Moscati, R. M. 2001. The Golden Hour: Scientific Fact or Medical “Urban Legend”?. Academic Emergency Medicine, 8: 758-760.
  39. Liaw, A., and Wiener, M. 2002. Classification and regression by random forest. R Newslett. vol. 2, no. 3, pp. 18-22.
  40. Lohani, D., and Acharya, D. 2016. Real time in-vehicle air quality monitoring using mobile sensing. 2016 IEEE Annual India Conference (INDICON). Bangalore. pp. 1-6.
  41. Martinez, F., Manzoni, P., Garrido, P., Fogue, M. 2012. Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles. IEEE Veh. Technol. Mag. 7, 90–100.
  42. McIver, G. W., Carlin, M. A., Bormann, J. E., Muckley, R. A., McCurdy, R. A. 1996. Method and apparatus for sensing a vehicle crash condition using velocity enhanced acceleration crash metrics. U.S. Patent 5 587 906.
  43. Mendes-Moreira, J., Soares, C., Jorge, A. M., and Sousa, J.F.D. 2012. Ensemble Approaches for Regression: A Survey. ACM Comput. Surv. 45, 10:1–10:40.
  44. Moulik, S., Majumdar, S. 2019. FallSense: An Automatic Fall Detection and Alarm Generation System in IoT-Enabled Environment. IEEE Sensors Journal. vol. 19, no. 19, pp. 8452-8459.
  45. NMEA 2020. National Marine Electronics Association. Severna Park, MD USA 21146 Retrieved February 05, 2020 from https://www.nmea.org/
  46. Ponte, G., Ryan, G. A., Anderson, R. W. G. 2016. An estimate of the effectiveness of an in-vehicle automatic collision notification system in reducing road crash fatalities in South Australia. Traffic Injury Prevention. 17:3, 258-263.
  47. Ren, Y., Zhang, L., and Suganthan, P. N. 2016. Ensemble classification and regression-recent developments, applications and future directions. IEEE Comput. Intell. Mag. 11, 41–53.
  48. S911 2020. GP TOYS Foxx S911 RC Truck. Retrieved February 05, 2020 from https://g-p.hk/gptoys-foxx-s911.html
  49. Sada, H., and Moriyama, H. 1998. Crash sensor. U.S. Patent 5 777 225.
  50. Sadek, S., Al-Hamadi, A., Michaelis, B., Sayed, U. 2010. A Statistical Framework for Real-Time Traffic Accident Recognition. Journal of Signal and Information Processing. Vol. 1 No. 1, pp. 77-81.
  51. SAFETY CONNECT, Toyota Motor Corporation. 2009 Retrieved on: 17-04-20 https://www.toyota.com/connected-services/
  52. Sánchez-Mangas, R., García-Ferrrer, A., Aranzazu de Juan, Arroyo, A.M. 2010. The probability of death in road traffic accidents. How important is a quick medical response?. Accident Analysis & Prevention. Volume 42, Issue 4, Pages 1048-1056, ISSN 0001-4575.
  53. Schapire, R. E. 1990. The Strength of Weak Learnability. Mach. Learning. 5:2, 197—227
  54. Seera, M., and Lim, C. P. 2014. A hybrid intelligent system for medical data classification. Expert Syst. Appl. vol. 41, no. 5, pp. 2239–2249.
  55. Šingliar, T., Hauskrecht 2010. Learning to detect incidents from noisily labeled data. Machine Learning. vol. 79, Issue 3, pp 335–354.
  56. Smolka, J., Skublewska-Paszkowska, M. 2016. A method for collision detection using mobile devices. 2016 9th International Conference on Human System Interactions (HSI). Portsmouth. pp. 126-132.
  57. Steenwijk M. D. et al. 2013. Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). Neuroimage Clinical, Vol. 3(3), Pp. 462-469.
  58. Steiner, P., Weidel, P., Küblbeck, H., Steurer, H. et al. 1997. Roll Over Detection. SAE Technical Paper 970606.
  59. SUZUKI CONNECT: Advanced Telematics Solution. 2018. Maruti Suzuki India Ltd.? Retrieved on: 17-04-20 https://www.marutisuzuki.com/corporate/technology/suzuki-connect/
  60. SYNC, Ford Motor Company. 2010 Retrieved on: 17-04-20 https://www.ford.com/technology/sync/
  61. WHS. 2019?. World health statistics 2019: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO.
  62. Wolpert, D. H. 1992. Stacked generalization. Neural Networks. 5(2), 241–259.
  63. Yang, B., Lei, Y., Yan, B. 2016. Distributed Multi-Human Location Algorithm Using Naive Bayes Classifier for a Binary Pyroelectric Infrared Sensor Tracking System. IEEE Sensors Journal, vol. 16, no. 1, pp. 216-223.
  64. Zhang, Z., He, Q. 2016. On-site traffic accident detection with both social media and traffic data. Proc. 9th Triennial Symp. Transp. Anal. (TRISTAN).