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

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.