Driver behavior profiling: An investigation with different smartphone sensors and machine learning
Name
Driver behavior profiling- An investigation with different smartphone sensors and machine learning.pdf
Size
2.42 MB
Format
Adobe PDF
Checksum (MD5)
c0eadb6df6106e3ad94a0020cb52f2a6
Author(s)
Ferreira Junior, Jair
Carvalho, Eduardo
Ferreira, Bruno V.
de Souza, Cleidson
Suhara, Yoshihiko
Pentland, Alex
Pessin, Gustavo
Date Issued
April 10, 2017
Publisher
PLOS ONE
Citation
Ferreira, J., Carvalho, E., Ferreira, B. V., de Souza, C., Suhara, Y., Pentland, A., & Pessin, G. (2017). Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS one, 12(4), e0174959.
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
MIT Connection Science (Research institute)
Terms of Use
Attribution-NonCommercial-ShareAlike 3.0 United States
Persistent DSpace Link
