This paper suggests an intelligent controller for an automated vehicle planning its own trajectory based on sensor and communication data. The intelligent controller is designed using the learning stochastic automata theory. Using the data received from on-board sensors, two automata (one for lateral actions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments, unlike adaptive control methods or expert systems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging results
Automated highway system (AHS); Automated guided vehicle systems; Intelligent control systems; Intelligent vehicle control; Machine learning; Probabilistic automata; Reinforcement learning; Stochastic learning automata
Artificial Intelligence and Robotics | Controls and Control Theory | Systems and Communications | Transportation | Urban Studies and Planning
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Unsal, Cem, Pushkin Kachroo, and John S. Bay. "Multiple stochastic learning automata for vehicle path control in an automated highway system." Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 29, no. 1 (1999): 120-128.
Bay, J. S.
Multiple stochastic learning automata for vehicle path control in an automated highway system.
IEEE Transactions on Systems, Man, and Cybernetics Part A, 29(1),