Multiple stochastic learning automata for vehicle path control in an automated highway system

This paper suggests an intelligent controller for an automated vehicle plan ning its own trajectory based on sensor and communication data. The intelli gent controller is designed using learning stochastic automata theory. Usin g the data received from on-board sensors, two automata (one for lateral ac tions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unm odeled stochastic environments, unlike adaptive control methods or expert s ystems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging results.