The rise of Software-Defined Vehicles (SDVs) marks a fundamental shift in automotive design, where software, rather than hardware, defines vehicle capabilities. This review explores the SDV paradigm within Intelligent Transportation Systems (ITS), emphasizing centralized computing, over-the-air (OTA) updates, and service-oriented architectures (SOA). Key enablers such as high-performance computing units, middleware platforms (e.g., AUTOSAR Adaptive), and digital twins support scalable, flexible software deployment and real-time functionality.
Artificial Intelligence (AI) and Machine Learning (ML) enhance features like predictive maintenance, driver personalization, and autonomous decision-making. However, SDVs also introduce complex challenges, including software validation, real-time performance, and system interoperability. Cybersecurity becomes critical, especially as vehicles interface with cloud platforms, edge computing, and Vehicle-to-Everything (V2X) networks.
The paper also examines lightweight virtualization, containerization (e.g., Docker), and compliance with global standards such as ISO 26262 (functional safety) and ISO/SAE 21434 (cybersecurity). For electric and autonomous vehicles, software-defined frameworks offer unique advantages in updateability, energy optimization, and adaptive control.
Synthesizing research from 2005 to 2024, this review identifies key trends and open challenges in building secure, resilient, and scalable SDV platforms. The goal is to support engineers, researchers, and policymakers in shaping the future of connected, intelligent mobility.