Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4968561 | Transportation Research Part C: Emerging Technologies | 2017 | 11 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Eleni I. Vlahogianni, Emmanouil N. Barmpounakis,