A Cloud-Based Environment-Aware Driver Profiling Framework using Ensemble Supervised Learning

Abdalla Abdelrahman, Hossam S. Hassanein, Najah Abu-Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Driver profiling is an emerging scheme that has a wide range of applications in the field of Intelligent Transportation Systems (ITS). Driver profiling is the real-time process of detecting driving behaviors and computing a driver's competence level based on detected behaviors. In this paper, a novel driver profiling framework is presented. A risk prediction model is hosted in the cloud to determine the risk associated with detected behaviors in specific driving environments. Risk values along with a driver's compliance to warnings are both utilized to compute a driver's risk profile. Using SHRP2 large-scale Naturalistic Driving (ND) dataset, the development of the risk prediction model is presented herein with the underlying sub-processes of data preprocessing, error analysis, and model selection. Validation results show that a developed randomized trees supervised learning model is proven to have a good tradeoff between bias and variance with evidently high performance results.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538680889
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
Duration: May 20 2019May 24 2019

Publication series

NameIEEE International Conference on Communications
Volume2019-May
ISSN (Print)1550-3607

Conference

Conference2019 IEEE International Conference on Communications, ICC 2019
Country/TerritoryChina
CityShanghai
Period5/20/195/24/19

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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