It is important to model vehicular tyre movements and their lateral-slip characteristics because they affect safety while steering. In this study, we have used an artificial neural network (ANN) for modelling to solve issues such as insufficient learning adaptation and poor computability. However, because of the number of network nodes and layers and the superposition principle, the identification rate was low and node positions were uncertain. Hence, BDI-agent technology was introduced to optimise the training process of ANNs. We investigated and compared the performances of two models - the magic-formula-based and ANN-based models - for analysing tyre forces under combined representations of longitudinal and lateral forces. Our simulation results showed that the ANN-based model with BDI-agent performed better than the magic-formula-based model, and its mean error was low at 1.5%.
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