Managing Transition to Autonomous Vehicles Using Bayesian Fuzzy Logic

Abstract

Automotive industry is currently facing two global changes. There is a transition from the vehicles with internal combustion engines to the various hybrid and fully electrical vehicles. Another transition is the introduction of more intelligence and communication capabilities what creates a trend toward the development and application of fully autonomous vehicles. Both transitions are complex and decisions on the optimal pathways depend on the big data, but also on incomplete and inconsistent knowledge and expectations. Considering the fuzziness of the global business, social, ethical, and other domains, the paper presents results of the investigation and the application of fuzzy logic in a decision-making process of the transition to autonomous vehicles, conducted by car manufacturers. It considers the crisp and fuzzy information, outcomes, and actions and compares the values of additional information.