Jul 2026· International Workshop on Active-Matrix Flatpanel Displays and Devices· pp. 15-17· 0 citations· 4 references
Abstract
The transition toward Software-Defined Vehicles (SDVs) is fundamentally reshaping the automotive display and Human-Machine Interface (HMI) landscape. Driven by the deep integration of Artificial Intelligence (AI), the evolution of electronic architectures, and escalating geopolitical tensions, the traditional “larger is better” display paradigm has reached its physical and functional limits. This paper proposes a novel strategic framework, “The Cockpit Decision Reset,” focusing on three critical pillars: supply chain resilience (Just-in-Case), design value prioritization, and HMI logic transformation. We analyze how these factors individually and collectively impact automotive cockpit strategies, shifting the industry from passive screens to proactive, AI-driven environments. Furthermore, this study provides quantitative market forecasts for emerging display technologies, projecting FALD/Mini LED LCD to reach 22.5 million units by 2030, and OLED to reach 11.8 million units by 2030. Concurrently, Micro LED is forecasted to emerge in 2028 with 1.0 K units, growing to 53.0 K units by 2030. New design features such as Panoramic Head-Up Displays (PHUD), Under-Display Cameras (UDC), and Smart Surfaces and Privacy Displays, are also evaluated as essential components of the next-generation cockpit. The proposed framework offers a comprehensive guide for OEMs and Tier 1 suppliers navigating the complex SDV ecosystem.
A dependency-aware OTA orchestration framework that addresses challenges in improving update success rates, efficiency in execution time and update requests through optimized scheduling, and feasibility in maintaining system-wide integrity by successfully reconciling stringent safety requirements and diverse update sensitivity constraints is proposed.
Juyeon Park, In-Young Ko· SIGSOFT FSE Companion· 0 citations
Abstract. The design of next-generation flight vehicles for space access and in-orbit operations requires agile and modular tools that can support the rapid prototyping of expendable and reusable vehicles. Since 2020, Politecnico di Torino has been developing the iDREAM methodology and toolset within the framework of the GSTP program under the supervision of the European Space Agency (ESA). In its initial iteration, iDREAM focused on two case studies: a Micro Launcher (ML) and a Human Landing System (HLS). The toolset is composed of three main modules to: (i) support conceptual design activities through ASTRID-H, (ii) enable early-stage assessment of economic viability via HyCost and, (iii) analyze technology sustainability through TRIS, the dedicated technology roadmapping module. Moreover, a unified and upgraded database, supported by an in-house developed Database Management Library, supports the operation of all modules within the integrated toolset. The ongoing second phase (iDREAM 2.0) marks a paradigm shift by extending the methodology to support reusable flight systems, including first and/or second stage vertical and/or horizontal landing launch vehicles (RLV), reusable re-entry vehicles (RREV), and hypersonic use cases and introducing a highly modular environment. A key innovation lies in the integration of reusable systems modelling within existing expendable-focused routines, reducing redundancy and maximizing tool adaptability. The adopted approach focuses on minimizing the number of routines required for design and analysis while maximizing the coverage of reusable configuration combinations. Following a detailed trade-off analysis, the final architecture includes six routines for ASTRID-H, three for HyCost, and a single unified routine for the technology roadmapping TRIS module. This paper presents the current status of the ongoing iDREAM 2.0 project, which is currently in the development phase and undergoing validation through a set of reference vehicles. These include reusable launch systems, with vertical and horizontal recovery, reusable re-entry capsules, and hypersonic vehicle, demonstrating the framework’s versatility in supporting the design of future reusable space systems.
Antonio Gregorio· Materials Research Proceedin...· 0 citations
Artificial Intelligence (AI)-enabled Autonomous Mobile Robots (AMRs) are transforming industrial operations across manufacturing and logistics - accounting for 65% of new deployments in 2025 and displacing traditional Automated Guided Vehicles (AGVs) through superior navigation, flexibility, and autonomous decision-making. This paper examines the evolution and integration of AMRs with Private 5G networks in industrial applications. Traditional connectivity options, namely: wired networks, Wi-Fi, and public cellular, cannot meet the mobility, reliability, and latency requirements of industrial AMR deployments. Private 5G networks address these limitations through deterministic performance, dedicated spectrum, and enterprise-grade security. Our comparative analysis establishes AMRs’ performance advantages over AGVs and Private 5G’s operational superiority versus alternative connectivity approaches. In this work, we examine the computing architectures supporting AMR systems (local, edge, centralized, and hybrid), each with distinct implications for safety-critical industrial deployments. The integration of AI/ML with distributed intelligence and human-robot collaboration requires consistent, low-latency communication, particularly for safety-critical and shared workflows. This AMR-Private 5G integration positions industrial operations at the threshold of Industry 5.0, enabling autonomous, adaptive systems that enhance operational flexibility, safety, and productivity.
Vanlin Sathya, Steve Toteda, Mehmet Yavuz et al.· IEEE Access· 0 citations
The automotive industry is evolving rapidly, but many supply chains still operate through disconnected systems that limit visibility, delay decisions, and reduce resilience. This research presents a practical blueprint for transforming traditional supply chains into an AI-native synchronized ecosystem.
Readers will gain insights into how Multi-Agent Artificial Intelligence (MAAI), Digital Twins, Reinforcement Learning, predictive analytics, and mathematical optimization can work together to synchronize production, supplier collaboration, inventory, warehousing, yard operations, and transportation within a unified enterprise architecture.
The paper goes beyond theory by introducing a vendor-neutral reference architecture, implementation roadmap, governance framework, and technology stack that organizations can adapt to their own digital transformation journey. It also identifies key operational inefficiencies observed across global automotive OEMs and demonstrates how AI can improve visibility, resilience, planning agility, and enterprise-wide decision-making.
I look forward to engaging with researchers, supply chain professionals, and industry leaders to exchange ideas and shape the future of intelligent automotive supply chains.
P. Mishra· International Journal For Mu...· 0 citations
The AI framework was successfully shown to reorder and present messages based on real-time context, improving the clarity and usefulness of information provided to the driver, and support a hybrid C-V2X architecture as a robust model for future smart highway deployments.
Abin Mathew, A. Sundar, Juntong Peng et al.· 0 citations
Industry 5.0 represents a paradigm shift toward human-centric, intelligent, and sustainable manufacturing systems. At the core of this transformation lies the Digital Twin (DT), a virtual replica of physical assets that enables real-time monitoring, simulation, and decision-making. This article presents a comprehensive meta-analysis of how DT technologies contribute to the realization of Industry 5.0 objectives across domains such as Smart Additive Manufacturing (SAM), Predictive Maintenance (PM), Cyber-Physical Cognitive Systems (CPCS), Intelligent Supply Chain (ISC), and Adaptive Scheduling (AS). By analyzing 125 peer-reviewed studies, we quantify the feature-wise attainment levels of Industry 5.0 and identify critical gaps in current implementations. The findings reveal that, while SAM exhibits the highest Industry 5.0 readiness, other features, such as cognitive systems, remain underdeveloped. The article concludes by outlining key research challenges and presenting a strategic roadmap to advance the real-world integration of DTs within Industry 5.0 frameworks.
Swati Lipsa, R. K. Dash, Korhan Cengiz et al.· PeerJ Computer Science· 0 citations
