A review of visual driver models for system identification purposes

The aim of this study was to find a realistic control-theoretic visual driver model for curve driving that does not only show simular performance as actual drivers but also applies the same inputs and uses the same information. The model structure must enable system identification and parameter estimation of the model parameters. A large number of existing and adapted models have been evaluated and simulated, and when possible, frequency response functions have been identified using two system identification methods. A significant part of the paper is devoted to review these models. The evaluation shows that two-point models comply best with all system identification requirements while still governing realistic driving behavior. It is recommended to investigate further the positioning and perception part of the two-point models using eye-tracking in driving experiments with real human drivers.