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Sensors 2017, 17(10), 2404; doi:10.3390/s17102404

Estimation of the Driving Style Based on the Users’ Activity and Environment Influence

Laboratory for Telecommunications, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana 1000, Slovenia
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Received: 6 September 2017 / Revised: 17 October 2017 / Accepted: 19 October 2017 / Published: 21 October 2017
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Abstract

New models and methods have been designed to predict the influence of the user’s environment and activity information to the driving style in standard automotive environments. For these purposes, an experiment was conducted providing two types of analysis: (i) the evaluation of a self-assessment of the driving style; (ii) the prediction of aggressive driving style based on drivers’ activity and environment parameters. Sixty seven h of driving data from 10 drivers were collected for analysis in this study. The new parameters used in the experiment are the car door opening and closing manner, which were applied to improve the prediction accuracy. An Android application called Sensoric was developed to collect low-level smartphone data about the users’ activity. The driving style was predicted from the user’s environment and activity data collected before driving. The prediction was tested against the actual driving style, calculated from objective driving data. The prediction has shown encouraging results, with precision values ranging from 0.727 up to 0.909 for aggressive driving recognition rate. The obtained results lend support to the hypothesis that user’s environment and activity data could be used for the prediction of the aggressive driving style in advance, before the driving starts. View Full-Text
Keywords: aggressive driving; user environment data; activity data; driving style prediction aggressive driving; user environment data; activity data; driving style prediction
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Sysoev, M.; Kos, A.; Guna, J.; Pogačnik, M. Estimation of the Driving Style Based on the Users’ Activity and Environment Influence. Sensors 2017, 17, 2404.

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