Engineering and Technology
| Open Access | Synergistic Integration of Deep Learning, Fuzzy Decision Systems, And Electromagnetic Compatibility Protocols for Resilient Advanced Driver Assistance Systems in Next-Generation Electric Vehicles
Dr. Marcus Benjamin , Department of Electrical Engineering and Information Sciences, University of Manchester, United KingdomAbstract
The convergence of Advanced Driver Assistance Systems (ADAS), electric propulsion, and high-speed intra-vehicular networking has necessitated a paradigm shift in automotive systems engineering. As vehicles transition toward fully autonomous operation, the complexity of sensor data processing must be balanced against the physical constraints of the automotive environment, specifically electromagnetic interference (EMI) and power electronics noise. This comprehensive study explores a multifaceted approach to system resilience. It examines the implementation of Deep Learning (DL) and Fuzzy Decision Trees for real-time anomaly detection and link adaptation in LTE/LTE-A and automotive WLAN networks, ensuring robust communication for perception systems. Simultaneously, the research addresses the hardware-level challenges of electromagnetic compatibility (EMC) in high-voltage battery systems and 10G automotive Ethernet. By analyzing the interaction between software-based adaptive control laws-specifically fuzzy-tree synergetic control for DC/DC converters-and hardware-level shielding strategies, this article establishes a unified framework for dependable automotive electronic architecture. The study utilizes extensive theoretical elaboration on evolutionary optimization, including Particle Swarm Optimization (PSO) and Genetic Algorithms, to refine fuzzy inference systems, providing a pathway toward visually lossless, low-latency image coding and reliable ADAS performance in electromagnetically polluted environments.
Keywords
Advanced Driver Assistance Systems, Electromagnetic Compatibility, Deep Learning, Fuzzy Decision Trees
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