A Study on switching AR-HMM driving behavior model depending on driver´s states

To offer appropriate information, predicting driver\´s behavior is effective because we can show information only when they are useful, such as missing stops or cross to turn. But driver\´s behavior depends on his or her mental states, so it is better to predict driver\´s behavior considering his or her states. In this paper we focused on driver\´s "hasty state", and induced it with quota to run using driving simulator. And we found that driver\´s "hasty state" has effect on AR-HMM model parameters such as gas pedal stroke and brake pedal stroke, and also on autonomic nervous activity. As a result, we could show possibility to improve prediction accuracy by switching driving behavior model depending on driver\´s states.