Driving analytics using smartphones: Algorithms, comparisons and challenges

作者：Vlahogianni Eleni I; Barmpounakis Emmanouil N^*

来源：Transportation Research Part C: Emerging Technologies , 2017, 79: 196-206.

DOI：10.1016/j.trc.2017.03.014

• 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.

• 出版日期2017-6