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Driving Activity Recognition of Motorcyclists Using Smartphone Sensor

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Intelligent Technologies and Applications (INTAP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1198))

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Abstract

Smartphone sensors ubiquitously provide an unobtrusive opportunity to develop solutions for road anomaly detection, driving behavior analysis, and activity recognition. Driver’s activity recognition is important for monitoring streets and narrow lanes where employed vehicles cannot get along. In this paper, smartphone sensor is used to monitor driving activity of motorcyclists. Motorcyclists are asked to follow a predefined path and gyroscope data is recorded from the phone, which is placed in motorcyclist pocket. Features are selected from twelve extracted statistical features from the recorded gyroscope data to classify four driving activities i.e., left turn, right turn, U-turn, and a straight path. Three different classifiers i.e., Bayes Net, random forest, and support vector machine are used to classify four motorcyclists driving activities. It is evident that the random forest classifies four motorcyclist driving activities with the highest accuracy of 86.51%.

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References

  1. Chen, Z., Yu, J., Zhu, Y., Chen, Y., Li, M.: D 3: abnormal driving behaviors detection and identification using smartphone sensors. In: 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 524–532. IEEE (2015)

    Google Scholar 

  2. Coenen, T.B., Golroo, A.: A review on automated pavement distress detection methods. Cogent Eng. 4(1), 1374822 (2017)

    Article  Google Scholar 

  3. Douangphachanh, V., Oneyama, H.: A model for the estimation of road roughness condition from sensor data collected by android smartphones. J JSCE, Ser. D3 (Infrastr. Plann. Manage.) 70(5), I\(\_\)103–I\(\_\)111 (2014)

    Google Scholar 

  4. Doycheva, K., Koch, C., König, M.: GPU-enabled pavement distress image classification in real time. J. Comput. Civ. Eng. 31(3), 04016061 (2016)

    Article  Google Scholar 

  5. Gawad, S.M.A., El Mougy, A., El-Meligy, M.A.: Dynamic mapping of road conditions using smartphone sensors and machine learning techniques. In: 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), pp. 1–5. IEEE (2016)

    Google Scholar 

  6. Gopalakrishnan, K.: Deep learning in data-driven pavement image analysis and automated distress detection: a review. Data 3(3), 28 (2018)

    Article  Google Scholar 

  7. Harikrishnan, P., Gopi, V.P.: Vehicle vibration signal processing for road surface monitoring. IEEE Sens. J. 17(16), 5192–5197 (2017)

    Article  Google Scholar 

  8. Kohavi, R., Sommerfield, D.: Feature subset selection using the wrapper method: overfitting and dynamic search space topology. In: KDD, pp. 192–197 (1995)

    Google Scholar 

  9. Li, F., Zhang, H., Che, H., Qiu, X.: Dangerous driving behavior detection using smartphone sensors. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 1902–1907. IEEE (2016)

    Google Scholar 

  10. Li, X., Goldberg, D.W.: Toward a mobile crowdsensing system for road surface assessment. Comput. Environ. Urban Syst. 69, 51–62 (2018)

    Article  Google Scholar 

  11. Lima, L.C., Amorim, V.J.P., Pereira, I.M., Ribeiro, F.N., Oliveira, R.A.R.: Using crowdsourcing techniques and mobile devices for asphaltic pavement quality recognition. In: 2016 VI Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 144–149. IEEE (2016)

    Google Scholar 

  12. Lu, D.-N., Ngo, T.-T.-T., Le, H.-Q., Tran, T.-T.-H., Nguyen, M.-H.: MDBR: mobile driving behavior recognition using smartphone sensors. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10449, pp. 22–31. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67077-5_3

    Chapter  Google Scholar 

  13. Lu, D.N., Nguyen, D.N., Nguyen, T.H., Nguyen, H.N.: Vehicle mode and driving activity detection based on analyzing sensor data of smartphones. Sensors 18(4), 1036 (2018)

    Article  MathSciNet  Google Scholar 

  14. Ma, C., Dai, X., Zhu, J., Liu, N., Sun, H., Liu, M.: DrivingSense: dangerous driving behavior identification based on smartphone autocalibration. Mob. Inf. Syst. 2017 (2017)

    Google Scholar 

  15. Ma, X., Wang, H., Xue, B., Zhou, M., Ji, B., Li, Y.: Depth-based human fall detection via shape features and improved extreme learning machine. IEEE J. Biomed. Health Inf. 18(6), 1915–1922 (2014)

    Article  Google Scholar 

  16. Seraj, F., Zhang, K., Turkes, O., Meratnia, N., Havinga, P.J.: A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 1169–1177. ACM (2015)

    Google Scholar 

  17. Silva, N., Shah, V., Soares, J., Rodrigues, H.: Road anomalies detection system evaluation. Sensors 18(7), 1984 (2018)

    Article  Google Scholar 

  18. Silva, N., Soares, J., Shah, V., Santos, M.Y., Rodrigues, H.: Anomaly detection in roads with a data mining approach. Proc. Comput. Sci. 121, 415–422 (2017)

    Article  Google Scholar 

  19. Singh, G., Bansal, D., Sofat, S., Aggarwal, N.: Smart patrolling: an efficient road surface monitoring using smartphone sensors and crowdsourcing. Perv. Mob. Comput. 40, 71–88 (2017)

    Article  Google Scholar 

  20. Vlahogianni, E.I., Barmpounakis, E.N.: Driving analytics using smartphones: algorithms, comparisons and challenges. Transp. Res. Part C: Emerg. Technol. 79, 196–206 (2017)

    Article  Google Scholar 

  21. Yi, C.W., Chuang, Y.T., Nian, C.S.: Toward crowdsourcing-based road pavement monitoring by mobile sensing technologies. IEEE Trans. Intell. Transp. Syst. 16(4), 1905–1917 (2015)

    Article  Google Scholar 

  22. Yu, J., Chen, Z., Zhu, Y., Chen, Y.J., Kong, L., Li, M.: Fine-grained abnormal driving behaviors detection and identification with smartphones. IEEE Trans. Mob. Comput. 16(8), 2198–2212 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan

    Aasim Raheel, Muhammad Ehatisham-ul-Haq, Anees Iqbal, Hanan Ali & Muhammad Majid

Authors
  1. Aasim Raheel
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  2. Muhammad Ehatisham-ul-Haq
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  3. Anees Iqbal
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  4. Hanan Ali
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  5. Muhammad Majid
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Corresponding author

Correspondence to Muhammad Majid .

Editor information

Editors and Affiliations

  1. Islamia University of Bahawalpur, Baghdad, Pakistan

    Imran Sarwar Bajwa

  2. Metropolitan University, Belgrade, Serbia

    Tatjana Sibalija

  3. University of Technology Malaysia, Johor Bahru, Malaysia

    Dayang Norhayati Abang Jawawi

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Raheel, A., Ehatisham-ul-Haq, M., Iqbal, A., Ali, H., Majid, M. (2020). Driving Activity Recognition of Motorcyclists Using Smartphone Sensor. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_59

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  • DOI: https://doi.org/10.1007/978-981-15-5232-8_59

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5231-1

  • Online ISBN: 978-981-15-5232-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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