A driver model with higher precision is created as an evaluation standard for a driving condition in a normal condition. Further, a driving action is estimated using a driver model which can be created easily and can represent driving characteristics of a driver more precisely. By detecting biometric information of a driver, whether a driver is in a usual condition or not is recognized. Then, data of driving conditions (own vehicle information such as, for example, operation amounts of accelerator, brake, and steering wheel, vehicle speed, inter-vehicle distance, acceleration, and the like) are collected while the driver is driving, and from the driving condition data, a part indicating that the driver operates in a usual condition is extracted to create a driver model. Thus, without making the driver aware, a driver model for normal times can be created automatically. Further, the driver model is created taking only a case of driving in a normal condition as a driving action in normal times based on biometric information of the driver, and hence the driver model becomes more precise and neutral. Further, by using a GMM (Gaussian mixture model) for the driver model, a driver model for each driver can be created easily, and moreover, by calculation to maximize a conditional probability, a driving operation action is easily estimated and outputted.
DRIVE BEHAVIOR ESTIMATING DEVICE, DRIVE SUPPORTING DEVICE, VEHICLE EVALUATING SYSTEM, DRIVER MODEL MAKING DEVICE, AND DRIVE BEHAVIOR JUDGING DEVICE