AI-Enabled Semantic Modeling for Enhanced Boardnet Integration in Automotive Design

The integration of artificial intelligence (AI) techniques in the automotive industry has revolutionized various aspects, including object identification, hazard recognition, speech recognition, and driver assistance. In this scientific paper, we propose a novel approach that leverages AI to enhance the integration of boardnet design in the automotive domain. The primary objective of the boardnet is to ensure reliable power distribution, efficient energy management, data communication, effective sensor integration, precise actuator control, and the integration of advanced features while prioritizing safety, reliability, and optimal performance of the vehicle's electrical system.Our proposed model utilizes inference-based AI techniques, incorporating both external and internal routing AI within a semantic framework. By aligning the model with a top-level ISO 26262 standard definitions ontology, we establish a systematic systems and electronics semantic framework that seamlessly integrates with existing design processes. The model accommodates the expertise of system engineers and knowledge engineers, enabling the harmonious integration of their distinct approaches.Furthermore, this paper explores the automation of design process gaps through the deduction of valuable information. By employing OWL DL 2 and logical axioms, we demonstrate the reasoning capabilities of our approach, highlighting its advantages in terms of speed, usability, and integration within the overall design process.The integration of AI and semantic modeling in boardnet design facilitates intelligent decision-making, optimization, and automation. The semantic framework enables a comprehensive understanding of the boardnet and its components, improving the efficiency and effectiveness of the design process. The proposed approach contributes to the advancement of automotive design and development practices, enhancing power distribution, energy management, data communication, sensor integration, actuator control, and the integration of advanced features.To validate the effectiveness of our approach, we conducted a series of experiments and evaluations. The results demonstrate that AI-enabled semantic modeling significantly improves the boardnet integration process. It facilitates improved power distribution, makes the energy management more pervasive, the data exchange among components seamless, and precises regulation of actuators. Moreover, the integration of advanced features becomes smoother, providing enhanced functionalities to vehicles while maintaining safety, reliability, and optimal performance.Additionally, we highlight the practical implications of our research by discussing real-world use cases. By using property classifications, inverse object properties, property chains and complex class expressions, the design process is amended by filling additional and complementary information which reduces development time. The benefits extend to increased vehicle performance, and reduced maintenance requirements.In conclusion, this paper establishes the significance and potential of AI-enabled semantic modeling for boardnet integration in automotive design. By leveraging AI techniques and semantic frameworks, engineers and researchers can achieve superior design outcomes, drive innovation, and meet the evolving demands of the automotive industry. The experimental results and real-world use cases presented herein provide a solid foundation for further exploration and adoption of AI in boardnet integration, contributing to the advancement of automotive technologies and the realization of intelligent, efficient, and reliable vehicles.