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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The road to bringing FSD to Europe has been a long and complex one and filled with regulatory and bureaucratic hurdles. Elon Musk, as well as other members of Tesla’s AI team, have previously voiced their grievances with the regulatory approval process on X.
However, it appears that there is finally some progress in getting things moving with recent changes to upcoming autonomy regulations, but the process still seems slow.
Waiting on the Dutch
Elon commented on X recently, stating that Tesla is waiting for approval from Dutch authorities and then the EU to start rolling out FSD in Europe. Tesla is focusing on acquiring approvals from the Dutch transportation authority, which will provide them with the platform they need to gain broader acceptance in Europe. Outside of the Netherlands, Tesla is also conducting testing in Norway, which provides a couple of avenues for them to obtain national-level approval.
The frustration has been ongoing, with multiple committee meetings bringing up autonomy regulation but always pulling back at the last second before approving anything. The last meeting on Regulation 157, which governs Automated Lane Keeping Systems, concluded with authorities from the UK and Spain requesting additional time to analyze the data before reaching a conclusion.
Tesla, as well as Elon, have motioned several times for owners to reach out to their elected representatives to move the process forward, as it seems that Tesla’s own efforts are being stymied.
This can seem odd, especially since Tesla has previously demoed FSD working exceptionally smoothly on European roads - and just did it again in Rome when they shared the video below on X.
— Tesla Europe & Middle East (@teslaeurope) June 12, 2025
DCAS Phase 3
While the approval process has been slow, Kees Roelandschap pointed out that there may be a different regulatory step that could allow FSD to gain a foothold in Europe.
According to Kees, the European Commission is now taking a new approach to approving ADAS systems under the new DCAS Phase 3 regulations. The Commission is now seeking data from systems currently operational in the United States that can perform System-Initiated Maneuvers and don’t require hands-on intervention for every request.
This is key because those are two of the core functionalities that make FSD so usable, and it also means that there may not be a need to wait years for proper regulations to be written from scratch. Now, the Commission will be looking at real-world data based on existing, deployed technology, which could speed up the process immensely.
What This Means
This new, data-driven regulatory approach could be the path for Tesla to reach its previous target of September for European FSD. While the cogs of bureaucracy are ever slow, sometimes all it takes is a little data to have them turn a bit faster in this case.
Alongside specific countries granting approval for limited field testing with employees, there is some light at the end of the tunnel for FSD in Europe, and hopes are that a release will occur by the end of 2025. With Europe now looking to North America for how FSD is performing, Tesla’s Robotaxi results could also play a role.
Tesla’s marketing has always been relatively unconventional, relying on word-of-mouth rather than traditional advertising. The passion of the owner’s community is always massive, but it is especially high now with the launch of the Robotaxi network just around the corner.
Tesla is now tapping into that spring of fan creativity and announced the TeslaVision video contest, with some seriously impressive prizes up for grabs.
Announcing the TeslaVision fan video showcase
Tesla owners & supporters have always been able to see our products & mission for what they truly are.
Your word of mouth has made Tesla what it is today.
The OGs will remember that in 2017, we held our first video contest. 8 years… pic.twitter.com/6pPpkqmqOH
The core of the contest is simple. Create a video that shows how your Tesla gives you more in life. Tesla is looking for submissions that highlight themes of freedom, safety, fun, and convenience.
Prizes
The prizes definitely make this contest worth entering if you’re good with a camera and have some basic video editing abilities.
For North America, the prizes include a brand new Model Y AWD Long Range, alongside an all-expenses-paid trip to Austin for a tour of Giga Texas. The grand prize winner will also be able to custom order their Model Y, allowing them to select their preferred wheels and color.
The two runners-up won’t get a Model Y, but they’ll also enjoy an all-expenses-paid trip to Giga Texas for a tour of the factory.
The travel and tour include lodging in Austin for 2 nights, as well as economy-class round-trip tickets from anywhere in North America. Tesla will also provide a vehicle for use during the trip.
For long-time followers of Tesla, this contest may feel familiar. The contest is a direct throwback to the 2017 Project Loveday contest, which was inspired by a letter to Tesla from a 10-year-old aspiring marketer. That contest was won by MKBHD, with his submission below:
How to Enter
If you’re ready to start filming, here are the key pieces of information you’ll need to know:
Video must be 90 seconds or less
Video must be uploaded to YouTube with a public URL
Make a post on X and Instagram tagging “@Tesla” and include the words “TeslaVision contest” in the post.
Provide links to both social media posts in your submission to Tesla’s form
Provide your personal details in the form
You have until July 17th, 2025, or until Tesla receives 10,000 entries, whichever comes first.