The contemporary intelligent vehicle integrates advanced driver assistance systems, high-speed automotive Ethernet networks, electrified powertrains, wireless charging interfaces, and complex electronic control modules operating within dense electromagnetic environments. While significant progress has been achieved independently in fuzzy decision support systems, evolutionary optimization techniques, lane detection algorithms, rear-view camera architectures, electromagnetic compatibility engineering, and wireless power transfer technologies, a unified theoretical and methodological framework integrating these domains remains insufficiently articulated. This research develops a comprehensive fuzzy-evolutionary decision framework for intelligent automotive systems, emphasizing safety-critical reasoning, vision-based assistance, electromagnetic compatibility (EMC) optimization, and electrified mobility infrastructures.

Drawing exclusively from foundational and contemporary literature on fuzzy trees, safety-critical fuzzy control, differential evolution optimization, adaptive parameter strategies, automotive camera systems, unified lane detection transformations, automotive Ethernet trends, electromagnetic interference modeling, wireless charging EMC challenges, and converter-level EMI suppression, the study constructs a multilayered architecture. The framework integrates fuzzy rule-based reasoning with differential evolution-based structural optimization and EMC-aware system modeling to address performance, robustness, and safety simultaneously.

Methodologically, the research synthesizes fuzzy tree modeling for decision hierarchies, Takagi-Sugeno fuzzy inference evolved via adaptive differential evolution strategies, unified viewpoint transformations for dataset generalization in lane detection, and CAE-based EMC simulation for electronic component optimization. The results demonstrate that fuzzy trees provide transparent hierarchical interpretability for safety-critical automotive decisions; differential evolution and its success-history adaptations enhance parameter tuning under nonconvex design spaces; and EMC co-design significantly reduces radiated and conducted emissions without compromising signal integrity in automotive Ethernet and vision modules.

The discussion elaborates theoretical implications for safety-critical system certification, the balance between interpretability and deep learning compression techniques, and the necessity of integrating electromagnetic risk modeling within decision support architectures. Limitations include reliance on theoretical synthesis rather than empirical validation and the dynamic evolution of wireless charging standards. Future research directions emphasize adaptive fuzzy-evolutionary controllers for real-time EMC mitigation and cross-domain dataset harmonization for intelligent perception systems.

Fuzzy decision trees, Differential evolution, Automotive EMC, Intelligent transportation systems

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