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This work describes an approach to develop a model of driver's distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (Adaptive Neuro Fuzzy Inference System) technique. Further investigations will be carried out by porting the model on real car.

This paper proposes a method which allows user interfaces practitioners to choose the most suitable software tool for the development of a Human Machine Interface (HMI) virtual prototype. In the design process of a User Interface (UI), the prototyping activity represents an important way to detect design errors since the beginning of the development process, saving time and money. The choice has to take care of the technical features, designers' requirements and prototyping characteristics of the tool. The methodology includes four steps: 1) research on the most diffused virtual prototyping software employed in the prototyping activity; 2) benchmark on the technical and virtual prototyping features of each selected tool; 3) creation of a method to asses and classify each prototyping tool considering their specific characteristics; 4) application of the method to a case study.