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Zonal Architectures

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Automotive architectures are evolving quickly from domain-based to zonal, leveraging the same kind of high-performance computing now found in data centers to make split-second decisions on the road. The zonal architecture centralizes processing using 7nm and 5nm technology, specialized accelerators, high-speed memory, and highly targeted software architectures. For decades, most of the electronics on a car were encased in electronic control units (ECUs), segmented by function such as braking and infotainment. As more safety features were added and centralized, they were organized by distinct software stacks and automotive OSes based on different domains communicating with each other through a centralized gateway, which is what most new vehicles use today.

But as more autonomy is added into vehicles, the latency of a centralized gateway is proving unworkable. Tighter interdependence, scalability, and flexibility are all required, which a zonal architecture allows, and OEMs are at varying stages of adopting this approach. Strikingly, the automotive zonal architectures look a lot like scaled-down HPC data centers.

Zonal architectures are key to autonomous driving, and a big part of this involves sensors. Processing needs to happen in real-time, particularly for safety-related decisions, which is why there is so much emphasis on high-performance compute capability.

As the automotive industry transitions toward software-defined vehicles and zonal architectures, the approach to sensor fusion is evolving. OEMs are working to integrate these new systems with their existing architectures, which presents significant challenges. The goal is to create a more centralized and scalable system that can be easily updated and expanded as technology advances.

It’s worth noting that the specific implementation of sensor fusion can vary significantly between manufacturers, with some opting for more centralized approaches while others distribute processing across multiple nodes in the vehicle.

OEMs now realize that incremental improvements to ADAS and IVI won’t achieve the desired results, and a more fundamental shift to zonal architectures is necessary. However, the transition isn’t uniform across all regions. While there is not a 100% correlation between SDV and zonal architectures, zonal architectures greatly simplify many complex tasks required for effective SDV implementation.

OEMs also want to reduce the cost of building and testing vehicle prototypes, and this is where simulation can play a critical role. Adding AI can help leverage learnings from past designs, as well. And both will be essential in a broad shift toward zonal architectures and SDV, which are necessary for long-term competitiveness.

Traditional OEMs aim to reduce the number of ECUs from about 100 to 30 to 40, which would result in significant cost savings and greatly simplified networks. No one doubts the transition will happen. The question now is when that will happen.

Not every vehicle model can be updated simultaneously, and OEMs often take a staged approach, implementing partial steps towards full zonal architectures across their fleet. While there is pressure to move quickly, OEMs are cautious about over-promising and under-delivering, especially given past hype cycles around autonomous vehicles. They are striving to balance innovation with realistic timelines and deliverable promises.

A final consideration is legacy network protocols versus newer options such as Ethernet. Any new design will need to function at least as well as the old ones. The real challenge is implementing a smooth transition without impacting new product introductions. As a result, OEMs may have different degrees of zonal transformation and timetables. Startups or new OEMs may have more flexibility in adopting a zonal architecture than OEMs with legacy designs.

Related Reading
Zonal Architecture Development with evolution of Artificial Intelligence
This paper explains how traditional centralized architectures are transitioning to distributed zonal approaches to address challenges in scalability, reliability, performance, and cost-effectiveness.

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