How Lenovo is scaling Level 4 autonomous robotaxis on Arm

After years of autonomous driving pilots and controlled trials, the automotive industry is moving toward the production-scale deployment of Level 4 (L4) robotaxis. This marks a significant moment for artificial intelligence (AI), as it moves from advising humans on recommended actions to enabling vehicles that perceive their environment, although it comes with a steep increase in technical demands.  

Compared with today’s advanced L2++ vehicles, L4 systems typically require broader sensor stack, such as LiDAR, cameras and radar, which drive data processing requirements from roughly 25GB per hour to as much as 19TB per hour. This has forced a fundamental rethink of compute for physical AI.    

To that effect, Lenovo has developed L4 Autonomous Driving Domain Controller AD1, a production-ready autonomous driving computing platform powered by dual Arm-based NVIDIA DRIVE AGX Thor chips. WeRide is deploying the platform in its GXR Robotaxi, which is the world’s first mass-produced L4 autonomous vehicles.

Inside Lenovo AD1

The Lenovo AD1 serves as the central brain inside the GXR Robotaxi, managing multiple functions from perception, prediction and trajectory planning, to real-time motion control and safety monitoring. It is reported that Lenovo AD1 can reduce system costs by around 50% and lower total cost of ownership by up to 84%, which marks a major step toward commercially viable autonomy at scale. 

Lenovo AD1 is a platform designed for production-grade L4 autonomy for robotaxis and other autonomous vehicles. Supporting over 2,000 TOPS of AI capacity, it enables dense perception, prediction, and planning models to run simultaneously for faster, better decision-making on the roads.  

For robotaxis, many loosely coupled electronic control units (ECUs) cannot deliver the latency, safety, or scalability L4 requires, so instead they need centralized, high-performance compute platforms. Therefore, AD1 is powered by NVIDIA DRIVE AGX Thor, a centralized car computer built on the Arm Neoverse V3AE CPU, which brings previously separate driving, parking, cockpit, and monitoring functions into one compute domain.  

Efficiency, safety, and foundation for physical AI

Arm serves as the foundational compute architecture of the NVIDIA DRIVE AGX Thor platform, enabling advanced computing capabilities that power Lenovo’s AD1 platform. 

1. Performance per watt for fleet economics: As robotaxis operate for extended hours in demanding dense urban environments, the Arm compute platform delivers server-class performance into a highly efficient power envelope, enabling large AI workloads without compromising vehicle battery or thermal design.
2. A safety-ready architecture: The Arm ecosystem – including functional-safety-capable technologies, toolchains, software solutions, and long-established automotive partners – supports the platforms designed to meet ASIL-D and other global safety requirements, a critical factor for long-lived commercial deployments.
3. A mature, scalable software ecosystem: Since Arm provides a unified architecture across cloud, edge and physical environments, it allows developers to build, optimize, and scale AI models using widely available software tools and frameworks.
4. A roadmap aligned with future AI workloads: As physical AI models continue to grow in size and complexity, compute efficiency and architectural stability become increasingly important. By building on Arm, automakers gain a consistent architectural foundation with a long-term roadmap and helps avoid future redesigns and keeping the compute strategy stable even as AI evolves.

The road to autonomy is being built on Arm

The deployment of Lenovo AD1 in WeRide’s GXR Robotaxis shows how physical AI in autonomous driving systems is moving beyond controlled pilots and into real, complex urban environments. As autonomous capabilities advance through L4 robotaxis and other autonomous vehicles, the industry is converging on platforms that deliver high performance, safety, and power-efficiency through a centralized architecture. 

Arm sits at the core of this shift, providing the foundation that enables companies like Lenovo and WeRide to run dense AI workloads continuously, adapt to rapidly evolving models, and support fleets that must operate reliably for years. As robotaxis expand into new cities and global markets, the Arm compute platform – built for safety and engineered to meet the real-world demands of physical AI at scale – is a critical part of the road ahead.