A B S T R A C T This paper attempts to demonstrate the applicability of artificial neural networks to the estimation of steel properties, cyclic strain-hardening exponent and cyclic strength coefficient, characterizing cyclic Ramberg-Osgood equation on the basis of monotonic tensile test properties. For this purpose, steel tensile data were extracted from the literature and two separate neural networks were constructed. One set of data was used for training the two networks and the remaining for testing purposes. Regression analysis and mean relative error calculation were used to check the accuracy of the system in the training and testing phases. Comparison of the results obtained from the neural networks and the values obtained from direct fitting of experimental data, indicated the reasonable prediction of cyclic strain-hardening exponent and cyclic strength coefficient, which are often used to characterize the cyclic deformation curve by a Ramberg-Osgood type equation.a i = network output value b = fatigue strength exponent b j = bias term associated with neuron j BHN = Brinell hardness c = fatigue ductility exponent E = modulus of elasticity f = nonlinear activation function K = cyclic strength coefficient MRE = mean relative error MSE = mean square error n = neuron n = cyclic strain-hardening exponent N = total number of training patterns p i = input signal generated for neuron i R = regression result RA% = percent reduction in area S u = ultimate tensile strength t i = target value w ji = weight from neuron i to neuron j Y j = output of neuron j ε = cyclic strain range ε/2 = cyclic strain amplitude Correspondence: R. Ghajar.

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Overtaking is a common driving maneuver and also the most complex one. Studying this maneuver is considered to be one of the toughest challenges in the development of autonomous vehicles. Here, a novel overtaking model based on adaptive neuro-fuzzy inference system is proposed. This model is designed for two vehicle classes: motorcycles and autos. The presented model is able to simulate and predict the trajectory of the overtaker vehicle in real traffic flow. In this model, important factors such distance, velocity, acceleration and the movement angle of the overtaker vehicle are considered. Using the field data, the performance of the model is validated and compared with the real traffic datasets. The results show very close compatibility between the field and model trajectory.

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