The automotive industry stands at a critical inflection point in the software-defined vehicle (SDV) transition. While the vision is clear, the path forward remains fragmented, with significant gaps between aspiration and execution. This presentation combines insights from three Omdia studies—the SDV Ecosystem Survey, the SDV Survey and Market Forecast—to deliver a data-driven assessment of where the industry stands today and what's holding back progress.

This session will address:

• What industry key stakeholders define as the key enablers of SDV – examining technological foundations, business model innovations, and organizational transformations that stakeholders identify as critical to the SDV transition
• How the implementation of these enabling technologies has progressed – assessing current deployment status and stakeholder expectations for when key SDV capabilities will be fully implemented across the industry
• Where the critical gaps for widespread deployment of SDVs are placed – identifying the technical, organizational, and ecosystem challenges that remain unresolved and are impeding scaled adoption
• Why measuring market progress is difficult without a common way to classify vehicles – discussing the lack of standardized benchmarking and the confusion created by inconsistent SDV definitions across the industry
• Introducing the SDV Levels framework – presenting an updated classification system that provides a common language for the industry to consistently categorize vehicles and track the transitio

The talk will conclude with an overview of the current state of the market and regional trajectories. Using the introduced framework, we will analyse deployments patterns and future projections across North America, EMEA and Asia.

Eclipse SDV is a global, vendor-neutral open-source ecosystem that serves as an ideal collaboration platform for startups and tech companies driving automotive innovation. It features Eclipse S-CORE as a pioneering middleware stack for safety-related use cases, proving that critical automotive functionality can be successfully developed within an open collaboration framework. Ultimately, this model demonstrates that robust neutral governance and cross-border alignment are key to navigating software sovereignty, highlighting the vital need for global collaboration across the industry.

• Automakers are investing heavily in vehicle operating systems, intelligent platforms, and continuously evolving digital experiences. Yet across the industry, up to 40% of program budgets can still be consumed by integration, testing, and validation work that repeats with every new program. That is engineering capacity spent rebuilding the base instead of advancing the experiences, intelligence, and features that differentiate the vehicle.
• The path forward is treating foundational software as a reusable platform, not a program deliverable. When integration, performance tuning, orchestration, and safety alignment are solved once at the foundation, they can be reused across programs, domains, ECUs, and vehicle lines. Alloy Kore is that shared foundation: an integrated automotive software layer beneath the Vehicle OS, helping OEMs build scalable, production-ready software platforms without recreating the wheel.

Since the introduction of high-performance compute platforms in the automotive domain, software architectures have been structured into board support package, (optionally) hypervisor, POSIX operating system, middleware, and application software - each component developed and optimized in isolation. With rising software integration complexity and considering modern development workflows this siloed approach is no longer sufficient. Reaching the next level of usability, performance, and reusability requires a more holistic approach. An approach in which boundaries between components deliberately blur.

The result is a novel architecture centered around a true Automotive OS, complemented by "Personalities": lightweight adapters for different software ecosystems. This enables cross-domain reuse of core functions, eliminates redundancy, and preserves the flexibility to support diverse application ecosystems as required, e.g. in IVI, ADAS, and vehicle dynamics. To prevent vendor lock-in and enable broad industry adoption, the Automotive OS API must be openly designed and governed. This keynote presents a reference architecture and outline a concrete path towards an open industry standard that benefits OEMs, Tier-1 suppliers, and software innovators alike.

The introduction of software defined vehicles has dramatically increased the complexity of automotive software, while simultaneously demanding rigorous functional validation.  Design teams often need numerous validation cycles through software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing to achieve a working product.  In this presentation we show how Code Metal’s automated test generation tools with formal verification can significantly decrease the number of validation cycles that are needed, allowing teams to shift left to resolve problems on the left side of the “Engineering V,” where the time and cost for iteration is significantly lower.  The result is a development process that reduces engineering risk and improves confidence in software transformations, allowing teams to bring products to market significantly faster.

Getting a driverless vehicle onto the road is one challenge. Scaling a safe, reliable robotaxi service across thousands of vehicles, cities and operating conditions is another. This session will examine the end-to-end computing challenge behind moving from initial deployment to fleet scale. It will explore how a three-computer architecture spanning AI training, simulation, and in-vehicle computing enables teams to continuously improve models, test the long tail of rare scenarios and deploy production-ready systems. The discussion will also consider how open, modular platforms allow mobility providers, autonomous-vehicle developers and automakers to tailor their technology stacks while building on a common foundation for scale.  Safe autonomous driving requires full-stack safety—from the vehicle and AI models to the development process—validated through both on road testing & physically accurate simulation.