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For many years, time series analysis methods have been applied to develop models linking serious road crashes with the effects of socio-economic and other factors. Classical time series models, such as log-linear regression or ARIMA, are useful in describing observed road trauma but are typically poor when used for forecasting road safety trends. In recent years, a new method of forecasting has been developed called an artificial neural network (ANN). The main focus of this research was on examining the possibility of using ANNs for prediction and to find complex relationships ‘hidden’ in time series models that classical modelling techniques often fail to reveal. Application of structural time series models for this purpose was also investigated.

The increasingly complex products and intelligent environment in automotive and transportation technology need to employ the intelligent tools called robots. To be able to solve a difficult and complex problem robot needs to cooperate with other robots. In this process experts from different disciplines utilize different form of knowledge that requires knowledge integration. In order to facilitate knowledge integration one of the forms of visualization can be used. Visual thinking is strictly connected with understanding of the visual forms. The aim of this research is to investigate the visual thinking capabilities of the intelligent systems in different aspects of the problem solving and communication abilities. This research is continuation of the authors previous work focused on investigating understanding capabilities of the intelligent systems based on the shape understanding system (SUS).