Neutrosophic model for vehicular malfunction detection

Abstract

The internet of vehicular things (IOVT) is an important modern technology that offers many advantages and facilities; however, if vehicular malfunctions are not detected in a timely manner, it may cause many dangers and serious accidents. To achieve safe self-driving vehicles, safety and security measures must be taken. In this work, a safety and security model are proposed to evaluate the level of vehicular malfunctions and determine the corresponding danger in terms of road safety. The proposed model presents the optimal actions and alternatives for self-driving vehicles to avoid crises. The objective of this study to develop a hybrid model for multicriteria decision-making problems using neutrosophic theory to handle vehicular malfunctions that occur in the IOVT environment under uncertain conditions and conflicting information. In addition, the technique for order of preference by similarity to the ideal solution is used to prioritize the corresponding alternatives in the case of vehicular malfunction. A case study considering four likely vehicular defects is presented to ensure the applicability and availability of the proposed model.

Keywords

• internet of vehicular things
• vehicular malfunction detection
• multi-criteria decision making
• neutrosophic theory
• analytical hierarchy process