Track: Decision Sciences

Abstract

Technological improvement for the safe and efficient use of self-driving vehicles continues at full speed. The development in this area in the coming years seems to affect human lives significantly. By the progress in Industry 4.0, products and hardware developed with software can exchange smart da-ta to ensure their management and optimization. It is possible to say that there will be much work about this technology shortly, even if they are not yet widely used. The investments of giant companies in this technology al-so support this development. However, the benefits and costs of autonomous vehicles are still mostly hypothetical. Self-driving cars layer their autonomy into six categories according to the capability of different levels of self-driving. Deciding on the autonomy of self-driving vehicles is a complicated procedure that requires various attributes to consider. Multi-Criteria Decision Making (MCDM) techniques are built to assist these kinds of decision-making problems. Fuzzy extensions aim to define judgments of decision-makers more explicitly and informatively by building on membership functions having three distinct dimensions. A new extension of ordinary fuzzy sets has been developed recently based on generalized three-dimensions called SFSs (Spherical Fuzzy Sets). In this study, the aim is to apply SF-TODIM (Spherical Fuzzy Tomada de Decisão Interativa Multicritério) MCDM method to evaluate autonomy selection in self-driving vehicles problem in a Group Decision Making environment. The autonomy selection in self-driving vehicles is assessed with five layers of autonomy using six criteria in order to demonstrate the applicability and validity of the developed SF-TODIM approach.