Driver behavior analysis based on Bayesian network and multiple classifiers

Driver behavior model is one of the key technologies for the driver assistance and safety system which can provide useful priori knowledge for detecting the deviant and dangerous behavior. This paper proposes the hybrid model based on Bayesian network and multiple classifiers of support vector machi...

Full description

Saved in:
Bibliographic Details
Published in2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Vol. 3; pp. 663 - 668
Main Authors Guoqing Xu, Li Liu, Zhangjun Song
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2010
Subjects
Acceleration
Adaptation model
Bayesian network
driver behavior model
Driver circuits
Hidden Markov models
multiple classifiers
support vector machine
Online AccessGet full text
ISBN9781424465828
1424465826
DOI10.1109/ICICISYS.2010.5658384

Cover

More Information
Summary:Driver behavior model is one of the key technologies for the driver assistance and safety system which can provide useful priori knowledge for detecting the deviant and dangerous behavior. This paper proposes the hybrid model based on Bayesian network and multiple classifiers of support vector machine to analyze and recognize the driver behavior and the limited and observable features of driver behavior are extracted in the model. In addition, the relationship between the features and driver behavior is analyzed. The effect of data loss on the hybrid model is also analyzed. Finally, the hybrid model is compared with support vector machine. Experiment results show that the hybrid model can achieve better accuracy and stability.
ISBN:9781424465828
1424465826
DOI:10.1109/ICICISYS.2010.5658384