Abstract
Driving is considered one of the most difficult tasks because the driver is responsible for a variety of other responsibilities in addition to driving. The primary responsibility of a driver should be to properly operate a vehicle while concentrating solely on driving. However, he/she must also complete various secondary jobs at the same time. For example, operating the steering wheel and the controls situated on the dashboard and steering wheel, operating the brake, accelerator, and clutch pedals while shifting gears as needed, and so forth. Modeling realistic driving behaviour proved tough for researchers and scientists. In this work, we examine the necessity for driver behaviour analysis as well as a method for visualising and estimating driver behaviour patterns utilising smart phone sensor data.
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References
Eftekhari, H.R., Ghatee, M.: A similarity-based neuro-fuzzy modeling for driving behaviour recognition applying fusion of smartphone sensors. J. Intell. Transp. Syst. 23, 72–83 (2019)
Baheti, B., Gajre, S., Talbar, S.: Detection of distracted driver using convolutional neural network. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2018)
Eftekhari, H.R., Ghatee, M.: Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behaviour recognition. Transp. Res. Part. F. 58, 782–796 (2018)
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, 1036 (2018)
Zinebi, K., Souissi, N., Tikito, K.: Driver behaviour analysis methods: applications oriented study. In: The 3rd International Conference on Big Data, Cloud and Applications – BDCA 2018, Morocco (2018)
Ferreira, J.Jr., et al.: Driver behaviour profiling: an investigation with different smartphone sensors and machine learning. PLOS ONE. 12, e0174959 (2017). https://doi.org/10.1371/journal.pone.0174959
Singh, S.K.: Road traffic accidents in India: issues and challenges. Transp. Res. Proc. 25, 4708–4719 (2017)
Ahuja, V.K.A.: Traffic and road safety management in India. Int. J. Res. Educ. Sci. Methods (IJARESM). 4(3), (2016). ISSN: 2455–6211
Munigety, C.R., Mathew, T.V.: Towards behavioral modeling of drivers in mixed traffic conditions. Transp. Dev. Econ. 2(1), 1–20 (2016). https://doi.org/10.1007/s40890-016-0012-y
Wu, M., Zhang, S., Dong, Y.: A novel model-based driving behaviour recognition system using motion sensors. Sensors. 16, 1746 (2016)
Liu, Z., Wu, M., Zhu, K., Zhang, L.: SenSafe: A Smartphone-Based Traffic Safety Framework by Sensing Vehicle and Pedestrian Behaviours. Hindawi Publishing Corporation Mobile Information Systems Volume (2016)
Meiring, G.A.M., Myburgh, H.C.: A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors. 15, 30653–30682 (2015)
Press Information Bureau: Government of India, Ministry of Petroleum and Natural Gas. https://pib.gov.in/newsite/printrelease.aspx?relid=102799
Paefgen, J., Kehr, F., Zhai, Y., Michahelles, F.: Driving Behaviour Analysis with Smartphones: Insights from a Controlled Field Study. ACM (2012)
Eren, H., Makinist, S., Akin, E., Yilmaz, A.: Estimating driving behaviour by a smartphone. In: Intelligent Vehicles Symposium (2012)
Amdahl, P., Chaikiat, P.: Personas as drivers: an alternative approach for creating scenarios for ADAS evaluation. Master thesis in Cognitive Science, Linköping University, Sweden (2007)
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Wawage, P., Deshpande, Y. (2022). Pilot Implementation for Driver Behaviour Classification Using Smartphone Sensor Data for Driver-Vehicle Interaction Analysis. In: Ardito, C., et al. Sense, Feel, Design. INTERACT 2021. Lecture Notes in Computer Science, vol 13198. Springer, Cham. https://doi.org/10.1007/978-3-030-98388-8_37
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DOI: https://doi.org/10.1007/978-3-030-98388-8_37
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