Abstract

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.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.