The automotive landscape is experiencing a paradigm shift, driven by the pervasive integration of artificial intelligence (AI) across all functional layers, the availability of previously unattainable data-processing capabilities, and an increasingly tight convergence of sensors, actuators, and advanced communication technologies. This perspective article explores the evolution from connected vehicles to Software (SW)-defined vehicles (SDVs), focusing on the emerging frontier of AI-defined vehicles (AIDVs) as a self-evolving architecture that integrates generative AI (GenAI) for scenario synthesis and agentic AI for autonomous decision-making. This article discusses the technology evolution and the role of AI and vehicle-to-everything (V2X) communication in enabling this transformation. The architecture of SDVs and the conceptual framework of AIDVs are examined, highlighting the transition from Hardware (HW)-centric to SW- and AI-centric designs. Recent advances and future directions are also discussed, presenting a multifaceted view of the opportunities and challenges by synthesising insights from research and industry trends and offering a forward-looking perspective on the technological, societal, and ethical factors shaping the evolution of automated intelligent transportation systems (ITSs).
Ovidiu Vermesan, Valerio Frascolla, Patrick Pype et al.· River Publishers eBooks· 0 citations
Edge AI systems are emerging from the convergence of IoT, edge computing, AI, agentic AI, and embodied, physical generative edge AI delivering adaptive, autonomous behaviour under physical, cyber, and operational constraints while remaining trustworthy. This article frames edge AI as a complex system-of-systems in which hardware, software, models, and data continuously co-evolve across heterogeneous “multi-X” environments: multiple systems, modalities, and agents distributed from the edge to the cloud. The article argues that as edge AI technologies are maturing, there is a need for a standardised, application-agnostic reference architecture to provide a shared lexicon and taxonomy, reduce integration errors, and expose opportunities for reusable assets and productive interoperability and standardisation. The paper grounds this need in systems engineering and introduces a quad-optimisation paradigm for balancing competing objectives during design and operation. The article presents a design framework and a multi-dimensional architecture organised into three complementary views: quality properties for trustworthiness and dependability, a layered technology stack within each tier, and a processing continuum that partitions intelligence across edge-to-cloud tiers. Finally, the article discusses value creation, interoperability, and how a 2 common baseline supports the development of complex edge AI systems-of-systems and their verification, validation, testing and benchmarking.
Ovidiu Vermesan, Marcello Coppola, Silke Braune et al.· River Publishers eBooks· 0 citations
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