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This paper surveys the landscape of Software-Defined Vehicles (SDVs), detailing the architectural shifts from ECU-based systems to centralized computing platforms. It categorizes SDV technologies into functional hardware, E/E architectures, software frameworks, automation mechanisms, and distributed infrastructure domains. The paper also addresses challenges like cybersecurity and interoperability within the Software-Defined Internet of Vehicles (SDIoV) paradigm.
SDVs are not just about software; they represent a complete architectural overhaul, demanding new approaches to cybersecurity, interoperability, and data management.
The transition toward Software-Defined Vehicles (SDVs) represents a major paradigm shift in vehicle design, transforming traditional hardware-centric systems into software-centric platforms capable of dynamic adaptation and continuous functional evolution. SDVs enable advanced capabilities such as Over-the-Air (OTA) updates, intelligent automation, and connected services driven by AI. This paper presents a comprehensive survey of the architectures, enabling technologies, and operational frameworks that define modern SDVs. It examines the evolution of vehicle architectures from distributed electronic control unit (ECU) systems to domain-based, zonal, and centralized computing platforms. Key enabling technologies are reviewed, including service-oriented software architectures, middleware, automation pipelines, artificial intelligence mechanisms, and cloud-based infrastructures. A structured taxonomy is introduced to organize SDV technologies into functional hardware, E/E architectures, software frameworks, automation mechanisms, and distributed infrastructure domains. The study also investigates the Software-Defined Internet of Vehicles (SDIoV) paradigm, integrating Software-Defined Networking (SDN) with edge and fog computing to support scalable vehicular communication and data processing. Furthermore, key technical challenges related to cybersecurity, interoperability, data management, and system scalability are discussed, along with emerging research directions and future development trends.