Document Type

Conference Proceeding

Abstract

One of the most important issues in Automated Highway System (AHS) deployment is intelligent vehicle control. While the technology to safely maneuver vehicles exists, the problem of making intelligent decisions to improve a single vehicle’s travel time and safety while optimizing the overall traffic flow is still a stumbling block. 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 (lateral and longitudinal) actions to avoid collisions. This learning method is capable of adapting to the automata environment resulting from an unmodeled physical environment. Although the learning approach taken is capable of providing a safe decision, optimization of the overall traffic flow is required. This is achieved by studying the interaction of the vehicles. The design of the adaptive vehicle path planner based on local information is extended to the interaction of multiple intelligent vehicles. By analyzing the situations consisting of conflicting desired vehicle paths, we extend our design by additional decision structures. The analysis of the situations and the design of the additional structures are made possible by treatment of the interacting reward-penalty mechanisms in individual vehicles as automata games. The definition of the physical environment of a vehicle as a series of discrete state transitions associated with a “stationary automata environment” is the key to this analysis and to the design of the intelligent vehicle path controller.

Disciplines

Artificial Intelligence and Robotics | Controls and Control Theory | Systems and Communications | Transportation | Urban Studies and Planning

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Permissions

©1997 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. "Simulation study of learning automata games in automated highway systems." In Intelligent Transportation System, 1997. ITSC'97., IEEE Conference on, pp. 936-941. IEEE, 1997.