Paper Info
Title
A comparative study of time series modeling for driving behavior towards prediction
Abstract
Prediction of driving behaviors is an important problem in developing a next-generation driving support system. In order to take diverse driving situations into account, it is necessary to model multiple driving operation time series data. In this study we modeled multiple driving operation time series with four modeling methods including beta process autoregressive hidden Markov model (BP-AR-HMM), which we used in our previous study. We quantitatively compared the modeling methods with respect to prediction accuracies, and concluded that BP-AR-HMM excelled the other modeling methods in modeling multiple driving operation time series and predicting unknown driving operations. The result suggests that BP-AR-HMM estimated behaviors of a driver and transition probabilities between the behaviors more successfully than the other methods, because BP-AR-HMM can deal with commonalities and differences among multiple time series, but the others cannot. Therefore BP-AR-HMM may help us to predict driver behaviors in real environment and to develop the next-generation driving support system.
Year
DOI
Venue
2013
10.1109/APSIPA.2013.6694284
Signal and Information Processing Association Annual Summit and Conference
Keywords
Field
DocType
autoregressive processes,hidden Markov models,probability,time series,traffic engineering computing,BP-AR-HMM,beta process autoregressive hidden Markov model,driving behavior prediction,multiple driving operation time series data modelling,next-generation driving support system,transition probability
Time series modeling,Autoregressive model,Time series,Support system,Computer science,Artificial intelligence,Hidden Markov model,STAR model,Machine learning
Conference
ISSN
Citations 
PageRank 
2309-9402
2
0.37
References 
Authors
6
4
Name
Order
Citations
PageRank
Ryunosuke Hamada171.54
Kubo, T.221.05
K. Ikeda324155.17
Zhengyou Zhang410208863.17