Simulation study of learning automata games in automated highway systems

We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible actions to avoid collisions. Although the learning approach taken is capable of providing a safe decision, optimization of the overall traffic flow is required. This can be achieved by studying the interaction of the vehicles. The design of the adaptive vehicle path planner based on local information is extended with additional decision structures by analyzing the situations of conflicting desired vehicle paths. The analysis of the situations and the design of these structures are made possible by treatment of the interacting reward-penalty mechanisms in individual vehicles as automata games.