DEVELOPMENT OF ALGORITHMS FOR COGNITIVE DECISION SUPPORT BY INTELLIGENT TRANSPORT SYSTEMS OPERATORS BASED ON THE ANALYSIS OF TYPICAL STRATEGIES AND THINKING ERRORS
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Abstract
The problem of human error in the control loop of intelligent transport systems is considered. The necessity of transition from traditional data-oriented decision support systems to a new class of systems capable of identifying the cognitive state of the operator, his strategies and predisposition to errors in real time is substantiated. A methodological approach to the development of such systems is proposed, based on the creation of a typology of cognitive errors, identification of their digital markers in the operator's behavior and the use of machine learning algorithms for the implementation of adaptive support interventions.
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