Abstract
This paper presents a study on the intelligent vehicle that has the driver's subjective evaluation model. In this system, inferring driver's subjective evaluation for drivability, the vehicle changes the control characteristics of the engine, the automatic transmission, etc. This study is the first step towards designing an intelligent vehicle capable of operating under specified driving conditions. The subject's driving task is to follow the car in front of subject and to keep the headway distance between the two cars consistent. In order to carry outthis driving task, drivers are asked to center the first car within the boundaries indicated on the windshield of the test car. The relationship between the error of headway distance and the accelerator of the subject who follows the first car are expressed by auto regressive and moving average models (ARMA model). Fuzzy data result from this relationship are generated from the coefficients of the ARMA model. Because each driver performs the given task differently, the data are difficult to analyze with conventional methodology. I applied techniques similar to that of type-2 fuzzy sets analysis to this data. This model are applied to predict subjective evaluation of the driver performing the driving task with a "control" car which has an unknown characteristic of automatic transmission, and the inference remains true.
