Prediction model of driving behavior based on traffic conditions and driver types
Hideomi Amata, Chiyomi Miyajima, Takanori Nishino, Norihide Kitaoka, Kazuya Takeda · 2009
We investigate the driving behavior differences at unsignalized intersections between expert and nonexpert drivers. By analyzing real-world driving data, significant differences were seen in pedal operations but not in steering operations. Easing accelerator behaviors before entering unsignalized intersections were especially seen more often in expert driving. We propose two prediction models for driving behaviors in terms of traffic conditions and driver types: one is based on multiple linear regression analysis, which predicts whether the driver will steer, ease up on the accelerator, or brake. The second predicts driver decelerating intentions using a Bayesian network. The proposed models could predict the three driving actions with over 70% accuracy, and about 50% of decelerating intentions were predicted before entering unsignalized intersections.