A Mathematical Neutrosophic Offset Framework with Upside Down Logics for Quality Evaluation of Multi-Sensor Intelligent Vehicle Environment Perception Systems

Abstract

The accuracy and reliability of intelligent vehicle perception systems 
significantly depend on the quality of data integration across multiple sensors operating 
in uncertain and contradictory environments. Traditional fusion models are often 
constrained by rigid logic systems and limited by probabilistic bounds, making them 
inadequate in dynamic or ambiguous contexts. This paper introduces a novel 
mathematical framework that combines Neutrosophic Offset Theory with Upside-Down 
Logics to evaluate and enhance the perception quality of autonomous systems. The 
proposed model allows sensor readings to exceed the conventional membership bounds 
[0,1], accommodating over-determined or negative-impact inputs through offset 
membership values. Moreover, by applying upside-down logic reasoning, we reinterpret 
contradictory sensor data within a broader logical spectrum. A set of mathematical 
definitions, operators, and formulations are presented, along with a complete case study 
simulating an urban driving scenario with conflicting sensor outputs. The framework 
quantitatively assesses perception quality using neutrosophic scores and shows enhanced 
robustness against uncertain, biased, or paradoxical information. The results confirm that 
the integration of offset-based neutrosophy and upside-down logic provides a flexible, 
logically consistent, and mathematically sound approach to perception quality analysis in 
intelligent vehicle systems.

DOI: 10.5281/zenodo.15758624