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

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 latera...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 1999-01, Vol.29 (1), p.120-128
Main Authors: Unsal, C., Kachroo, P., Bay, J.S.
Format: Article
Language:English
Subjects:
Adaptive control
Automated
Automatic control
Automation
Communication system control
Expert systems
Highways
Intelligent sensors
Intelligent vehicles
Learning
Learning automata
Longitudinal control
Sensors
Stochastic processes
Stochastic systems
Stochasticity
Trajectory
Vehicles
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Summary: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.
ISSN:1083-4427
1558-2426
DOI:10.1109/3468.736368