Peculiarities of justification of a representative set of requirements for intelligent vehicles

Abstract

Introduction (problem statement and relevance). The key task of ensuring confidence in motor vehicles with elements of artificial intelligence (AI vehicles) is forming of a representative set of requirements compliance with which ensures the necessary guarantees of AI vehicle functionality and safety.Absence of generally accepted and statutory approaches to justification of the requirements content constrains application of artificial intelligence technologies in motor vehicles and, respectively, impedes creation and improvement of highly automated vehicles. The purpose of the study is to analyze the principles of forming of a representative set of requirements for AI vehicles. Methodology and research methods. The paper uses the principles of qualimetry adapted for the information systems based on machine learning algorithms, it also uses the methods of system analysis, mathematical statistics, combinatorics, set theory and propositional calculus principles. Scientific novelty and results. The approach is formulated to justify the statistically representative set of requirements for AI vehicles, and that allows adequate and accurate assessment of compliance of these systems with the expectations of the customers, developers, suppliers, regulatory authorities and other stakeholders. The structure of such expectations and priorities has been considered taking into account creation and application of AI systems. It is shown that stakeholder requirements apply to both the AI vehicle life cycle processes and the systems per se taking into account particular (given) conditions of their operation. Different significant factors, which variability sets the intended operating conditions, have been considered and conditions of representativeness of test data sets used for AI vehicle trials have been formulated. It is shown that, in order to reduce the dimensionality of space of the intended operating conditions, AI vehicles can be decomposed into separate functional subsystems and then into individual AI algorithms. The ways to perform such functional decomposition have been suggested. Practical significance.The principles of justification of the requirements content offered in the paper can be used in certification testing of artificial intelligence algorithms that are used in highly automated vehicles.