A Fuzzy-Based System for Assessment of Recognition Error in VANETs.

This paper introduces a novel system for recognition error detection in Vehicular Ad Hoc Networks (VANETs) using Fuzzy Logic (FL). The proposed system leverages a comprehensive set of input parameters, including Internal and External Distraction, Driver’s Inattention, and Inadequate Surveillance, to effectively evaluate and mitigate potential errors in vehicle recognition and response. In order to recognize the critical role of driver behavior and external factors in shaping road safety, we employ FL to model the intricate relationships and uncertainties inherent in such contexts. By incorporating linguistic variables and a rule-based inference mechanism, the system transforms the multidimensional input parameters into actionable insights regarding the likelihood of recognition errors. The distinctive contribution of this research lies in its holistic consideration of both driver-related and external variables, encompassing a wide spectrum of influences on recognition accuracy. Through simulatio validation, our proposed system demonstrates its efficacy in capturing subtle variations in driver attention and environmental conditions. Ultimately, the FL-based recognition error system holds significant promise in advancing the capabilities of VANETs, paving the way for more adaptive and responsive vehicular communication systems that prioritize safety in dynamic road environments.