In our continued series exploring Tesla’s patents, we’re taking a look at how Tesla automates data labeling for FSD. This is Tesla patent WO2024073033A1, which outlines a system that could revolutionize how Tesla trains FSD.
We’ll be approaching this article the same way as others in the past, by breaking it down into easily digestible portions.
Training a sophisticated AI model like FSD requires a tremendous amount of data. But all of that data needs to be labeled - and traditionally, this process has been done manually. Human reviewers have to go in and categorize and tag hundreds of thousands of data points across millions of hours of video.
This isn’t just laborious and rote work, it's time consuming, expensive, and prone to human error. The perfect job to hand off to AI.
Tesla’s Automated Solution
Tesla’s patent introduces a model-agnostic system for automated data labeling. Just like their previous patent on the Universal Translator, this will function for any AI model - but FSD is really what it is for.
The system works by leveraging the vast amounts of data collected by Tesla’s fleet to create a 3D model of the environment, which is then automatically used to label new data.
Three Step Process
This process has three steps, so we’ll look at each individually.
High-Precision Mapping
The system starts by creating a highly accurate 3D map of the environment. This involves fusing data from multiple Tesla vehicles equipped with cameras, radar, and other sensors. The map includes detailed information about roads, lane markings, buildings, trees, and other static objects.
It's like creating a digital twin of the real world, and this is exactly the simulation data that Tesla uses to rapidly test FSD. The system continuously improves its accuracy as it processes more data and also generates better synthetic data to augment the training dataset.
Multi-Trip Reconstruction
To refine the 3D model and capture dynamic elements of the environment, the system analyzes data from multiple trips through the same area. This allows it to identify moving objects, track their trajectories, and understand how they interact with the static environment. This way, you have a dynamic, living 3D world that also captures the ebb and flow of traffic and pedestrians.
Automated Labelling
Once the 3D model is sufficiently detailed, it becomes the key to automated labeling. When a Tesla vehicle encounters a new scene, the system compares the real-time sensor data with the existing 3D model. This allows it to automatically identify and label objects, lane markings, and other relevant features in the new data.
Benefits
There are three simple benefits to this system, which is what makes it so valuable.
It is far more efficient. Automated data labeling drastically reduces the time and resources required to prepare training data for AI models. This accelerates development cycles and allows Tesla to train its AI on much larger datasets.
It is also scalable. This system can handle massive datasets derived from millions of miles of driving data collected by Tesla's fleet. As the fleet grows and collects more data, the 3D models become even more detailed and accurate, further improving the automated labeling process.
Finally, it is accurate. By eliminating human error and bias, automated labeling improves the accuracy and consistency of the labeled data. This leads to more robust and reliable AI models. Of course, human review is still involved, but that’s only to catch and flag errors.
Applications
While this technology has significant implications for FSD, Tesla can use this automated labeling system to train AI models for various tasks.
Object detection and classification: Accurately identifying and categorizing objects in the environment, such as vehicles, pedestrians, traffic signs, and obstacles.
Kinematic analysis: Understanding the motion and behavior of objects, predicting their trajectories, and anticipating potential hazards.
Shape analysis: Recognizing the shapes and structures of objects, even when partially obscured or viewed from different angles.
Occupancy and surface detection: Creating detailed maps of the environment, identifying occupied and free space, and understanding the properties of different surfaces (e.g., road, sidewalk, grass).
These different applications are all used by Tesla - which uses different AI subnets to analyze all these different things before feeding them into the greater model that is FSD, which means things like pedestrians, lane markings, and traffic controls are all labeled on-vehicle.
In a Nutshell
Tesla's automated data labeling system is a game-changer for AI development. By leveraging the power of its fleet and 3D mapping technology, Tesla has created a self-learning system that continuously improves its ability to understand and navigate the world.
Imagine a world where self-driving cars can label and understand the world around them without human help. This patent describes a system that could make that possible. It uses data collected from many Tesla vehicles to create a 3D model of the environment, which is like a virtual copy of the real world.
This 3D model is then used to label new images and sensor data, eliminating most needs for human intervention. The system can recognize objects, lane markings, and other important features, making it easier to train AI models.
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In a new video posted to X, Tesla is showing the progress of its first Lithium Iron Phosphate (LFP) cell manufacturing factory in North America. The facility, located in Sparks, Nevada, will be used to produce LFP battery cells for Megapacks and Powerwall.
However, the implications of this new factory extend beyond Tesla Energy. By on-shoring the production of these cost-effective batteries, Tesla is not only securing its energy supply chain but also opening the door to potentially reintroducing LFP-based vehicles in North America.
Nearing completion of our first LFP cell manufacturing factory in North America pic.twitter.com/OLNRWajz4l
The immediate beneficiary of the new Nevada LFP facility is Tesla’s Energy division. LFP chemistry is ideal for stationary storage products like Megapack and Powerwall. It offers a very long life cycle, is extremely thermally stable and safe, and is significantly cheaper to produce than nickel-based batteries, partly because it contains no cobalt.
Until now, Tesla has relied on suppliers like CATL in China for these cells. A dedicated, domestic supply will enable Tesla to dramatically ramp up Megapack production to meet North America’s increasing demand for grid-scale energy. On the other hand, Megafactory Shanghai continues to utilize CATL’s LFP batteries and will support the rest of the world.
Tesla first revealed that they were planning to onshore LFP production in North America at the Q1 2025 Earnings Call, which will help them avoid costs, innovate in new technology, and insulate themselves from geopolitical supply chain risks.
A Potential Return for LFP Vehicles?
Another exciting application for Tesla is what this new factory means for Tesla’s budget-oriented lineup. For years, Tesla has been constrained in its ability to offer LFP-based vehicles in North America. While LFP packs are used in other markets for specific standard-range RWD vehicles, tariffs on important Chinese cells made it difficult to import these cells for use in North America.
With a domestic supply of LFP cells produced in Nevada, this tariff-related barrier will be mostly eliminated, pending the sourcing of lithium from a North American site. This is likely to lead to the reintroduction of LFP-based vehicles to the North American market, possibly in late 2026 or 2027.
An American-made LFP pack could lead to a more affordable base Model 3 or Model Y, or potentially help Tesla cut costs on the next-generation Affordable Model even further. This helps to give customers a lower-cost entry point without sacrificing a lot of range, and with the added benefit of being able to regularly charge to 100%.
Mega Nevada
With Mega Nevada now progressing well, Tesla is in an excellent position to continue iterating on its vertical integration and scaling Megapack and Powerwall—two of Tesla’s fastest-growing businesses—further. There are tons of benefits for consumers in the future as Tesla continues down this path, with more affordable Powerwalls for the home, cheaper electricity prices thanks to grid-forming Megapacks, and cheaper LFP vehicles.
The next major upgrade for Tesla’s in-car experience is pretty much already here - just hiding beneath the surface, awaiting the flick of a switch. According to new details uncovered by Tesla hacker Greentheonly, a fully functional version of the Grok conversational AI assistant is already present in recent firmware builds, just waiting for Tesla to activate it.
The feature, which is currently behind a server-side switch, could be enabled at any time by Tesla for vehicles running update 2025.20 and newer. The findings provide a better picture of what we already learned from Green’s breakdown on Grok last month.
Grok’s Requirements
@greentheonly on X
According to what Green determined from the latest software builds, the foundation for Grok was laid with update 2025.14, with more abilities and functionality added in 2025.20 to flesh it out. He also determined exactly which vehicles will be receiving Grok.
In terms of hardware, any vehicle with a Ryzen-based infotainment computer will receive Grok. This means that vehicles with the older Intel Atom processor will not be supported, at least initially. The underlying Autopilot hardware is not a factor, as Grok’s processing is not done in-vehicle.
Grok will also require premium connectivity or a Wi-Fi connection for the vehicle. At this point, we’re not sure whether Grok in your Tesla will also require you to sign up for SuperGrok, X Premium, or X Premium+, but Tesla is requiring you to sign into your Grok account. It’s just not clear whether the free version of Grok will work, or if you’ll need the premium version.
Grok User Experience
@greentheonly on X
Green also revealed the user interface for Grok for the first time. You’ll find many of the same features from the Grok app, but surprisingly, it looks like it’ll have a dark UI, even if you’re using light mode in your vehicle.
It appears that there will be a Grok app, likely for settings. However, Grok will largely operate in a modal, similar to voice commands, which are displayed near the bottom left corner of the screen.
There’s an on-screen microphone button, as well as drop-down menus for the voice and type of assistant you’d like to use.
Similar to the Grok app currently on mobile devices, you’ll be able to select from a set of voices and then define their personality. The available voices for now are the standard Ara (Upbeat Female), Rex (Calm Male), and Gork (Lazy Male).
There’s also a settings button, which, when expanded, allows you to enable or disable NSFW mode (including swearing and adult topics), as well as a Kids Mode, which will tone Grok down to be suitable for when kids are in the car.
@greentheonly on X
How Grok Will Work (Button / Wake Word)
Users will be able to activate Grok by pressing a button, likely the same one that activates voice commands today. Grok will then remain enabled for the duration of your conversation, allowing you to go back and forth, asking and answering questions. To end your conversation, you’ll press the mic button again.
While it doesn’t appear to use a wake word yet, Green says that some code refers to a wake word, so it’s possible that this could be an option Tesla plans to activate in the future.
Replacing Voice Commands
The most significant implication of Grok’s future integration is in its potential to fully replace the existing and relatively rigid voice command system. Green notes that internally, this feature is part of the car assist module, and that eventually, the plan is for Grok to take over car control functions.
Unlike the current system, which requires specific phrases, a true conversational AI like Grok can understand natural language. This will enable more intuitive requests, completely changing how drivers interact with their car.
Language Support
@Greentheonly/X
Grok will also launch with multi-language support, similar to its current abilities in the Grok app. Green says that it already appears to have support for English and Chinese and one or two other languages.
Release Date
Grok appears ready to go from a vehicle standpoint, but Green wasn’t able to actually test it out. While development appears to be nearly complete in the vehicle, Tesla and xAI may still be working on some server-side changes to better integrate with the vehicle. If they plan for Grok to replace voice commands on day one, then it’ll need to be trained and be able to execute a variety of vehicle commands.
It’s possible Tesla is actively testing Grok or adding server-side changes to replace voice commands. However, it looks like vehicle development is nearly complete and Grok could launch as soon as the next major Tesla update, which is expected to be update 2025.24.
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