AI-Based Electronic Control Units for Software-Defined Vehicles: A Structured Review of Architectures, Techniques, and Hardware Implementations

The automotive electrical/electronic (E/E) architecture is undergoing a fundamental transformation from loosely coupled, function-specific electronic control units (ECUs) toward domain-centralized, zonal, and fully centralized computing architectures, forming the foundation of emerg- ing software-defined vehicles (SDVs). In parallel, artifi- cial intelligence (AI) and machine learning (ML) are mov- ing from predominantly cloud-based analytics toward di- rect deployment within automotive ECUs, enabling per- ception, prediction, diagnosis, cybersecurity, and adaptive control on resource-constrained embedded platforms. This paper presents a structured literature review of AI/ML- enabled ECU design within the SDV paradigm. It first introduces ECU fundamentals, functional roles, and in- vehicle networking, including a representative zonal SDV architecture integrating controller area network (CAN), CAN with flexible data-rate (CAN-FD), local intercon- nect network (LIN), and automotive Ethernet. The evolution from distributed to domain, zonal, and cen- tralized architectures is then discussed. Major ECU categories, including powertrain, transmission and en- ergy management, battery management systems (BMSs), chassis and safety, advanced driver assistance systems (ADAS) and autonomous driving, body control, infotain- ment and connectivity, and gateway, zonal, and central- compute ECUs, are systematically reviewed using func- tional diagrams, AI/ML techniques, datasets, and rele- vant literature. The paper also examines AI/ML train- ing, optimization, compression, validation, and deploy- ment on automotive system-on-chips (SoCs), with em- phasis on the automotive open system architecture (AU- TOSAR) adaptive platform, edge-AI accelerators, over- the-air (OTA) updates, field-programmable gate array (FPGA)-accelerated inference, and application-specific in- tegrated circuit (ASIC)-based implementations. Finally, key challenges in functional safety, real-time determin- ism, explainability, resource constraints, cybersecurity, and reliable software updates are discussed, followed by future research directions for efficient and hardware-aware AI/ML integration in SDV-oriented ECU architectures.