Document Type



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



©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Publisher Citation

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.

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