期刊
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷 79, 期 -, 页码 196-206出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2017.03.014
关键词
Driver's behavior; Critical driving patterns; Smartphones; Rough set theory; Machine learning
The present work investigates the use of smartphones as an alternative to gather data for driving behavior analysis. The proposed approach incorporates i. a device reorientation algorithm, which leverages gyroscope, accelerometer and GPS information, to correct the raw accelerometer data, and ii. a machine-learning framework based on rough set theory to identify rules and detect critical patterns solely based on the corrected accelerometer data. To evaluate the proposed framework, a series of driving experiments are conducted in both controlled and free-driving conditions. In all experiments, the smartphone can be freely positioned inside the subject vehicle. Findings indicate that the smartphone-based algorithms may accurately detect four distinct patterns (braking, acceleration, left cornering and right cornering) with an average accuracy comparable to other popular detection approaches based on data collected using a fixed position device. (C) 2017 Elsevier Ltd. All rights reserved.
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