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 language | English |
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Pages (from-to) | 1-6 |
Number of pages | 6 |
Journal | Proceedings - IEEE Global Communications Conference, GLOBECOM |
Volume | 2018-January |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore Duration: Dec 4 2017 → Dec 8 2017 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing