In this work some experiments are made in order to assess the feasibility of a human-like ACC (Adaptive Cruise Control) system. The proposed system is able to understand driver's attitudes and driving-styles by means of a selfcalibration process that can be (re)initialized on request. Three different speed-control logics have been tested: one tries to learn from actual drivers' behaviors by using an Artificial Neural Network (ANN) approach, the second is based on the calibration of a linear function aimed to be mimic of the driver response to stimuli, the third is based on the calibration of a polynomial function instead of a linear one. A microscopic traffic model, accurately calibrated and validated for different aims and in a previous work, has been adapted and used in order to generate a long car-following trajectory on which the speed control logics have been calibrated and compared. This has allowed for a sufficient amount of accurate laboratory data at a relatively low cost. Comparison of the tested speed-control logics show that a fully adaptive human-like ACC system is feasible and worth further more costly developments
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