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Driving analytics using smartphones: Algorithms, comparisons and challenges
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DOI：10.1016/j.trc.2017.03.014.png)
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Abstract
En 中文
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.
Keywords:
Driver's behavior
Critical driving patterns
Smartphones
Rough set theory
Machine learning
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