Use of Active Learning Method to Develop an Intelligent Stop and Go Cruise Control

S.A. Shahdi and S.B. Shouraki (Iran)


Intelligent vehicles, Active learning method, cruise control, fuzzy controller


This paper is concerned with the design and simulation of an intelligent stop and go cruise control system in an automated vehicle. In this paper Active learning method is used to extract driver's behavior and to derive control rules for cruise control system. First, there is a brief introduction to ALM (Active Learning Method) and its specifications. Then a one-line space for driving is assumed and its parameters are extracted. By using IDS, the processing engine of ALM, effective parameters in controller are derived. A simulation program is written to produce learning samples and also to evaluate controller's parameters. To apply controller's output, appropriate acceleration of the vehicle, by gas and brake pedals; motor and power transmission system model are introduced and their relations are calculated.

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