Automotive System Architecture Strategy for Bridging Long-Lifecycle Hardware & Fast-Evolving Software in SDV-AI Era
Abstract
The Software-Defined Vehicle (SDV) paradigm is reshaping automotive electronic architectures by enabling continuous software rollout & artificial intelligence (AI) evolution on platforms over the total service life of the hardware. This creates a fundamental challenge for electronic control unit (ECU) design: to enable sustained deployment, adoption & maintenance of AI models over vehicle’s lifetime. This paper presents a technology independent framework for integrating AI lifecycle management into SDV ECU architectures, while decoupling AI & software innovation from hardware constraints. It further discusses the role of scalable computing platforms based on high-performance CPUs, GPUs & SoCs, in supporting increasing AI workloads over time. In addition, the evolution of AUTOSAR-based hardware abstraction mechanisms is examined as an enabler for software portability & hardware independence. The discussed framework addresses key lifecycle aspects, including AI model deployment, versioning, rollout strategies & long-term maintainability within established automotive safety, cybersecurity frameworks. The presented work contributes to future-ready SDV systems capable of sustaining continuous AI innovation over extended operational lifetime.
