Name
Abstract | ||
|---|---|---|
Prediction of driving behaviors is important problem in developing the next-generation driving support system. In order to take account of diverse driving situations, it is necessary to deal with multiple time series data considering commonalities and differences among them. In this paper we utilize the beta process autoregressive hidden Markov model (BP-AR-HMM) that can model multiple time series considering common and different features among them using the beta process as a prior distribution. We apply the BP-AR-HMM to actual driving behavior data to estimate VAR process parameters that represent the driving behaviors, and with the estimated parameters we predict the driving behaviors of unknown test data. The results suggest that it is possible to identify the dynamical behaviors of driving operations using BP-AR-HMM, and to predict driving behaviors in actual environment. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638168 | 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
driving behavior prediction, Bayesian nonparametric approach, beta process autoregressive hidden Markov model, beta process | Autoregressive model,Time series,Markov model,Computer science,Test data,Artificial intelligence,STAR model,Prior probability,Hidden Markov model,Machine learning,Hidden semi-Markov model | Conference |
ISSN | Citations | PageRank |
1520-6149 | 3 | 0.46 |
References | Authors | |
6 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryunosuke Hamada | 1 | 7 | 1.54 |
| Takatomi Kubo | 2 | 20 | 7.61 |
| K. Ikeda | 3 | 241 | 55.17 |
| Zujie Zhang | 4 | 5 | 0.83 |
| Tomohiro Shibata | 5 | 256 | 49.49 |
| Takashi Bando | 6 | 123 | 14.55 |
| Masumi Egawa | 7 | 18 | 2.94 |