Driver Behavior Classification in Crash and Near-Crash Events Using 100-CAR Naturalistic Data Set

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

Research output: Contribution to journalConference articlepeer-review

13 Citations (Scopus)

Abstract

Recently, several car insurance companies got interested in classifying the behavior of drivers. Usage-based insurance (UBI), such as Pay-How-you- Drive (PHYD) scheme, is an innovative idea in which the insurance premium changes based on the driving behavior. This behavior is usually evaluated in terms of vehicle-related variables such as distance, speed, and acceleration to determine the expected risk profile for drivers. In this paper, an additional level of classification in the hierarchy of profiling is proposed. Using the 100-CAR naturalistic driving study (NDS) data set, five different Hidden Markov Models (HMMs) are trained to determine the fault responsibility of a Subject Vehicle (SV) in a crash or near-crash events. Two specific driving situations, which are conflicts with leading and following vehicles, are investigated in this study. Results show that these models can achieve a reasonable classification accuracy.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
Volume2018-January
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
Duration: Dec 4 2017Dec 8 2017

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

Fingerprint

Dive into the research topics of 'Driver Behavior Classification in Crash and Near-Crash Events Using 100-CAR Naturalistic Data Set'. Together they form a unique fingerprint.

Cite this