Driver behavior profiling: An investigation with different smartphone sensors and machine learning

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...

Full description

Bibliographic Details
Main Authors: Ferreira, Jair, Carvalho, Eduardo, Ferreira, Bruno V., de Souza, Cleidson, Suhara, Yoshihiko, Pessin, Gustavo, Pentland, Alex Paul
Other Authors: Massachusetts Institute of Technology. Media Laboratory
Format: Article
Language:en_US
Published: Public Library of Science 2017
Online Access:http://hdl.handle.net/1721.1/109964
https://orcid.org/0000-0002-8053-9983
Description
Summary: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.