Vision-only FSD vs. sensor fusion: Tesla's eyes vs. Nvidia's arsenal — who's winning the self-driving wars?

Jensen Huang, CEO of Nvidia, has repeatedly praised Tesla's Full Self-Driving (FSD) system in public statements.

Huang lavished praise on Tesla's FSD, describing it as "completely world-class" and "100% state-of-the-art." 

He also said Tesla's FSD is "the most advanced autonomous vehicle stack in the world," emphasising that Tesla has tackled the self-driving challenge end-to-end — from camera inputs to vehicle actuation — using a vision-only approach powered by neural networks and AI.

At the CES 2026, Huang highlighted Tesla's strategy, where a single large model is trained holistically using vast real-world data, simulation, and synthetic generation. 

Tesla uses Nvidia’s hardware

This vertical integration, he notes, allows Tesla to refine its system internally, drawing from billions of miles of driving data.

Huang also highlighted Tesla's long-term investment in the technology, noting that they’ve been refining it for years with a relatively small team, achieving extraordinary results by building a fully integrated system on Nvidia’s hardware.

What Huang thinks about pure vision-based self-driving AI

He argued that Tesla’s pure vision-based method (relying solely on cameras without additional sensors like lidar or radar) is "highly effective" as it mimics human driving, which primarily uses eyes for perception, and leverages massive real-world data from Tesla’s fleet to train models that handle complex scenarios. 

This data advantage allows Tesla to address the "long tail" of rare edge cases more efficiently than competitors, making their stack difficult to criticise and positioning them far ahead in the race for true autonomy.

What is Alpamayo

Nvidia's Alpamayo, in contrast, is a modular, versatile platform designed not to build cars but to empower the entire industry, said Huang at CES 2026 in Las Vegas.

In a nutshell is a family of open-source vision-language-action (VLA) AI models designed specifically for autonomous vehicle development. 

Nvidia provides training computers, simulation tools, and onboard systems that customers like Tesla (for training), Waymo, Nuro, Lucid, and Uber can customise.

Difference with Tesla’s FSD

Unlike Tesla's vision-only FSD, Alpamayo incorporates a multi-sensor fusion approach, combining cameras with radar and lidar to process driving scenes, reason through decisions, and even verbalise logic for better interpretability and auditing.

It includes massive open datasets (over 1,700 hours of driving data from multiple sensors across 25 countries) and simulation tools to help developers train and validate models for long-tail challenges, shifting from reactive systems to ones that apply "human-like thinking" for rare or novel situations.

Huang called it “the ChatGPT moment for physical AI”, aiming to enable Level 4 autonomy where vehicles can explain their actions, which could aid regulatory approval and user trust.

The rationale for pursuing Alpamayo despite high praise for Tesla's vision-only stack lies in Nvidia’s business model and strategic positioning. 

Nvidia isn’t building cars

Nvidia isn’t building cars or competing directly as a vehicle manufacturer like Tesla; instead, it’s a horizontal technology platform provider that supplies chips, software, and reference systems to a broad ecosystem of automakers, developers, and partners (e.g., Mercedes-Benz).

Huang explained that this makes Nvidia's approach “pervasive”, allowing widespread adoption across the industry rather than being tied to one company's fleet. 

What Huang thinks about Musk’s vision-only self-driving strategy

While Huang views Tesla’s pure-vision method as optimal for their integrated, data-rich ecosystem, he said Alpamayo’s sensor fusion (vision + lidar + radar) caters to diverse needs – such as enhanced safety in varied environments, better explainability for non-expert users or regulators, and faster development for companies without Tesla's scale of real-world data. 

He argued that Tesla’s pure vision-based method (relying solely on cameras without additional sensors like lidar or radar) is highly effective because it mimics human driving, which primarily uses eyes for perception, and leverages massive real-world data from Tesla's fleet to train models that handle complex scenarios. 

This data advantage allows Tesla to address the "long tail" of rare edge cases more efficiently than competitors, making their stack difficult to criticize and positioning them far ahead in the race for true autonomy.

'Open-source' Alpamayo: What it means

In general, "open source" tech refers to software, hardware, or processes with source code or designs that are publicly accessible, allowing anyone to inspect, modify, enhance, and distribute them.

Developed collaboratively, it promotes transparency, community-oriented development, and free access, often licensed to encourage innovation and shared improvement. 

Huang said Nvidia Alpamayo is a designed as comprehensive, open-source family of AI models, simulation frameworks, and datasets designed to accelerate the development of safe, reasoning-based autonomous vehicles (AVs)

It is specifically aimed at achieving Level 4 autonomy by enabling vehicles to "think" like humans — interpreting complex, rare, or long-tail driving scenarios rather than relying solely on pre-programmed rules. 

By making Alpamayo “open-source”, Nvidia aims to accelerate industry-wide progress, democratize access to advanced AV tech, and position itself as the backbone for "physical AI" in cars, robotaxis, and beyond — complementing rather than directly rivaling Tesla's vertical integration.

Tesla’s Elon Musk, in response, downplayed immediate competition, noting challenges in distribution and solving "long-tail issues" could delay Nvidia’s impact by 5-6 years, while wishing them success.

What Huang said about autonomous driving

He emphasises Nvidia’s pervasiveness, open-sourcing models to enable widespread adoption, and predicts autonomous tech will “explode”, with hundreds of millions of vehicles gaining capabilities in the next decade. "Everything that moves should be autonomous," he concludes.

Huang’s admiration for Musk underscores the symbiotic-yet-competitive dynamic between Nvidia and Tesla in AI-driven mobility, sparking debates on whether end-to-end or modular approaches will dominate. 

As autonomous tech accelerates, such public endorsements highlight the sector's collaborative future amid fierce innovation.

Takeaways:

• The battle for autonomous supremacy rages between Tesla's pure-vision FSD (cameras only, neural net magic) and sensor fusion stacks (lidar + radar + cameras)

• They are far from perfect, and will perhaps never achieve 100% perfection in the real world, especially alongside human drivers and the unpredictability of nature.

• Both are undergoing intensive real-world testing with limited commercial rollout.

• Nvidia's CES 2026 Alpamayo VLA models dropped open-source multi-sensor fusion, while Tesla's FSD v14 pushes vision-only to Level 4 edge cases.