Driver behavior profiling: An investigation with different smartphone sensors and machine learning
Name
Ferreira-2017-Driver behavior profiling_ An in.pdf
Size
2.2 MB
Format
Adobe PDF
Checksum (MD5)
44a24ef0ce85cfb23300f57cd718ca36
Author(s)
Ferreira, Jair
Carvalho, Eduardo
Ferreira, Bruno V.
de Souza, Cleidson
Suhara, Yoshihiko
Pessin, Gustavo
Pentland, Alex Paul
Date Issued
April 2017
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Ferreira, Jair; Carvalho, Eduardo; Ferreira, Bruno V.; de Souza, Cleidson; Suhara, Yoshihiko; Pentland, Alex and Pessin, Gustavo. “Driver Behavior Profiling: An Investigation with Different Smartphone Sensors and Machine Learning.” Edited by Houbing Song. PLOS ONE 12, no. 4 (April 2017): e0174959 © 2017 Ferreira et al
Version
Final published version
Abstract
Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0174959
