Modeling Driver Behavior at Intersections with Takagi-Sugeno Fuzzy Models

Due to the relatively high density of vehicles and humans at intersections, it is crucial for an Advanced Driver Assistance System (ADAS) to predict human driver behaviors to avoid crashes. Due to the complexity of human's behavior interacting with a vehicle, it is very difficult to find an explicit model to analysis the driver's behavior. In this paper Takagi-Sugeno is used as a data driven technique to model and predict driver's behavior at intersections. In the proposed technique, a Takagi-Sugeno model is trained for each maneuver using a Gath-Geva clustering based algorithm. The proposed models are then evaluated with real time experimental data, and the estimation results are presented.