Fuzzy modeling for vehicle maneuver detection in a scene

One of the goals of Advanced Driver Assistance Systems (ADASs) is to identify the role of a vehicle in a scene even without Global Positioning System (GPS) information. Some researches solve the problem by implementing different kinematic models for the vehicle along with a mechanism to decide the most suitable model at the current instant. In this work, a Fuzzy Rule Based Classification System (FRBCS) takes such decision starting from the measures coming from different sensors in the vehicle. The FRBCS is obtained through Data Driven Fuzzy Modeling (DDFM) techniques. In fact, several FRBCSs with promising results are generated. Most discovered models achieve better classification rates than previous researches while being simpler. Therefore, they are more suitable for implementation into an ADAS. Besides, some FRBCSs are far simpler while achieving similar rates. Finally, some tests have been done. They show the feasibility and suitability of this approach behind different situations.