The Future of Head Unit: Is Centralization enough?

KEY TAKEAWAY: Centralized architecture is becoming a key foundation for SDVs, combining potential efficiency gains with the integrated control required for expanding software and services.

Jongha Lee | Senior Professional, Product Planning, LG Vehicle Solution

One of the biggest catalysts behind the shift toward centralized architecture was Tesla, which demonstrated that this approach could be implemented at mass-production scale. Subsequent benchmarking and analysis highlighted the potential benefits of moving toward a centralized structure, including reductions in vehicle weight and Bill of Materials (BOM) costs. These advantages helped accelerate adoption across the automotive industry.

Since then, OEMs have continued to research and develop centralized architectures that best fit their own requirements. The transition toward SDVs is further reinforcing this direction. As software updates and services continue to expand, architectures that enable more integrated control and management are becoming increasingly important.

KEY TAKEAWAY: Common middleware can support software decoupling, but OEMs need to balance industry-wide standardization with their own development pace and differentiation through applications and AI.

Taesuk Yoon | Leader, HPC Advanced Development, LG Vehicle Solution

One important area is software decoupling. Industry consortiums are working to develop common middleware that can reduce dependency between software and underlying systems.

In Europe, initiatives such as S-CORE¹⁾ are pursuing this direction, while Japan has similar efforts through JASPAR²⁾. The aim is to standardize and jointly develop common middleware, allowing OEMs to concentrate more of their resources on applications and AI-enabled features that create value for customers.

The challenge is that these consortiums involve many stakeholders, which can make progress slower than expected. Their development pace may not always align with how quickly individual OEMs want to move.

OEMs therefore need to manage this gap carefully while maintaining their focus on the areas where they can create differentiated customer value.

1) S-CORE: Eclipse Safe Open Vehicle Core
2) JASPAR: Japan Automotive Software Platform and Architecture

KEY TAKEAWAY: AIDV architecture requires more than computing power. It must coordinate AI workloads, prioritize critical functions, and move sensor data efficiently within the resources available in each vehicle.

Jungyong Lee | Leader, Cabin AI Development, LG Vehicle Solution

In the AIDV era, multiple AI models for functions such as voice recognition, driver monitoring, and Advanced Driver Assistance Systems (ADAS) will operate simultaneously inside the vehicle. The challenge is therefore not simply to increase the performance of the central Electronic Control Unit (ECU).

Different AI models need to be appropriately allocated across the vehicle's Neural Processing Unit (NPU) resources and managed so that critical functions operate reliably when they are needed. Safety-critical AI functions such as driver monitoring and ADAS need to take priority even when the system is under heavy load and remain available whenever required.

Data flow is equally important. AI models need fast access to data from cameras, vehicle signals, and other sensors, making efficient data-transfer mechanisms such as zero-copy increasingly important. Ultimately, computing resources and data paths need to be designed together to balance cost, performance, and scalability.

KEY TAKEAWAY: There is no single architecture for every vehicle. OEMs are differentiating their computing strategies by segment to balance functionality, performance, and cost.

Jongha Lee | Senior Professional, Product Planning, LG Vehicle Solution

Rather than insisting on a purely centralized structure, OEMs are taking different approaches based on their own requirements. One consideration is cost. Applying High Performance Computing (HPC) systems can significantly increase the overall system cost, particularly when additional components such as liquid cooling are required.

As a result, OEMs are adopting different computing strategies across vehicle segments. High-end vehicles can use Multi-SoC-based HPC systems to support more centralized architectures, while mass-market vehicles may retain existing domain-based architectures or adopt lighter computing structures that place greater emphasis on cost efficiency. Instead of applying the same high-performance HPC system across every vehicle, another option is to tailor the computing architecture around the level of functionality each vehicle actually requires.

In response to these market needs, solutions such as LG HPC Lite can provide an approach that balances performance and cost. Similar trends are emerging in China, where the transition toward SDVs and AIDVs is moving rapidly.

Ultimately, the industry is evolving away from applying a single architecture to every vehicle and toward architectures tailored to different vehicle characteristics and market requirements.

KEY TAKEAWAY: A common, hardware-independent platform can make growing architectural complexity manageable by enabling software reuse and scalability across vehicle lineups.

Taesuk Yoon | Leader, HPC Advanced Development, LG Vehicle Solution

The key is making complexity manageable. As architectures become more diverse across different vehicles, it becomes increasingly important to establish a common platform structure that can be shared across multiple vehicle models.

Reducing dependency on specific hardware is one part of this approach. A more software-centric structure can make software easier to reuse even as underlying vehicle architectures differ.

At the same time, the platform needs to be scalable and reusable across a wide range of vehicle lineups. Rather than attempting to remove all architectural complexity, the goal is to create a structure in which that complexity can be effectively managed.

KEY TAKEAWAY: The Head Unit is evolving from an ECU into an AI Runtime Platform, making platform orchestration and AI engineering increasingly important sources of competitiveness.

Jungyong Lee | Leader, Cabin AI Development, LG Vehicle Solution

In the AIDV era, HPC needs to go beyond being simply an ECU. It needs to evolve into an AI Runtime Platform that runs and coordinates in-vehicle services.

The key is not only how well each individual AI function performs, but how reliably multiple AI functions can operate together within limited computing resources. This requires strong platform capabilities in areas such as resource scheduling, sensor data-path design, and runtime stability.

Suppliers will also need to identify AI models suited to the vehicle environment, continuously improve them using real-world vehicle data, and build the AI engineering capabilities needed to validate both performance and stability. Ultimately, the competitiveness of future Head Unit solutions will depend on understanding both the AI models and the platforms they run on, and reliably delivering the required AI services within the computing resources available.

The future of vehicle architecture will not be defined by a single approach. As vehicles evolve from SDVs to AIDVs, different vehicle segments and market needs will continue to require different combinations of centralized and distributed computing.

What will matter is how effectively these architectures can support a growing range of AI models and services, while balancing performance, cost, and scalability.

This is where the role of the Head Unit continues to evolve, from providing computing power to becoming a platform that can reliably run, coordinate, and continuously improve AI services in real-world vehicle environments.

Jaeho Lee | Moderator, Cabin AI Development, LG Vehicle Solution