Application of stochastic learning automata to intelligent vehicle control
Florin Stoica, Emil M. Popa · 2007
Abstract:- A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. This response may be favourable or unfavourable. The aim is to design an automaton that can determine the best action guided by past actions and responses. Using Stochastic Learning Automata techniques, we introduce a decision/control method for intelligent vehicles, in an infrastructure managed architecture. The aim is to design an automata system that can learn the best possible action based on the data received from on-board sensors or from the localization system of highway infrastructure.