Paper Info
Title
Accident Prediction System Based On Hidden Markov Model For Vehicular Ad-Hoc Network In Urban Environments
Abstract
With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle's velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.
Year
DOI
Venue
2018
10.3390/info9120311
INFORMATION
Keywords
Field
DocType
accident prediction system, driver assistance system, hidden markov model, VANET, ITS, HMM, ADAS
Crash,Computer science,Advanced driver assistance systems,Latent variable,Operator (computer programming),Artificial intelligence,Wireless ad hoc network,Hidden Markov model,Machine learning,Vehicular ad hoc network,The Internet
Journal
Volume
Issue
ISSN
9
12
2078-2489
Citations 
PageRank 
References 
3
0.41
5
Authors
4
Name
Order
Citations
PageRank
Nyothiri Aung1243.10
Weidong Zhang2257.24
Sahraoui Dhelim3627.20
Yibo Ai494.29