Architectural evolution of software-defined vehicles: AI/ml-driven models for intelligent mobility
Independent Researcher.
Research Article
World Journal of Advanced Research and Reviews, 2020, 06(02), 256-274
Article DOI: 10.30574/wjarr.2020.6.2.0101
Publication history:
Received on 19 April 2020; revised on 09 May 2020; accepted on 12 May 2020
Abstract:
The automotive industry is undergoing a major transformation with the emergence of vehicles defined by software (SDVs)-Vehicles that depend on software-oriented architectures rather than traditional hardware-based systems. This change has opened the door to Artificial Intelligence (AI) and machine learning (ML) to play a crucial role in forming the future of mobility. This article explores how these technologies boost advances in autonomous direction, predictive maintenance, fusion of sensors, and personalized mobility experiences. By leveraging border computing, cloud integration, and V2X communication (vehicle to everything), SDVs are becoming smarter, adaptable, and more efficient.
However, despite these innovations, several challenges remain. Questions such as computational limitations, cyber safety risks, regulatory obstacles, and the lack of standardization throughout the sector have significant barriers to generalized adoption. The approach to these challenges will require research, collaboration, and the development of continuous policies. Looking to the future, emerging technologies such as neuromorphic computing, digital twins, and AI models with energy efficiency should further increase SDV's abilities. This article comprehensively analyzes this evolving scenario, offering information on how AI/ML-oriented advances are shaping the next generation of smart mobility solutions.
Keywords:
Software-Defined Vehicles; Artificial Intelligence; Machine Learning; Intelligent Mobility; Autonomous Driving; Sensor Fusion; Edge Computing; V2X Communication; Predictive Maintenance; Digital Twins; Sustainable AI; Cybersecurity in These Automotive Systems
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0