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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Ashok Elluswamy, Tesla's Vice President of Autopilot and AI Software, recently discussed Tesla's artificial intelligence programs' current state and future ambitions. He covered FSD and then extended it to the broader topics of robotics and Artificial General Intelligence (AGI).
Journey to Truly Autonomous Driving
At the core of Tesla’s AI efforts lies the quest for fully autonomous vehicles. Ashok reiterated the long-term vision where, eventually, all newly manufactured cars are expected to be self-driving, with older, human-driven cars potentially becoming items for specialized hobbies or unique purposes.
However, he did acknowledge that the current advanced driver assistance systems (ADAS), including Tesla’s own FSD, require better reliability before the human can be completely removed from the equation.
The development process, he emphasized, is fundamentally rooted in machine learning rather than traditional programming. A crucial aspect of this is that AI is consistent across every vehicle, learning collectively from the fleet’s experiences rather than being unique to each car.
Progress in AI is continuous.
Safety and reliability remain Tesla’s focus for FSD. Now, with Tesla just weeks away from launching its Robotaxi Network in Austin, Texas, this is more true than ever, as any accidents could cause a delay in the program’s expansion or stop the program entirely.
No LiDAR
Ashok confirmed that Tesla still has no interest in LiDAR while discussing Tesla's vision-based sensor suite. He reiterated that cost and scalability remain key concerns with LiDAR, adding that its perceived usefulness diminishes as vision-based systems continue to improve.
Beyond the Road: FSD and Robotics
Ashok described Tesla’s AI network poetically - a “digital living being.” This emphasizes the organic way FSD absorbs information from the environment and learns from it. But FSD isn’t just for cars. Tesla uses FSD, as well as the same AI4 hardware from its vehicles, for its humanoid robot, Optimus.
Ashok expects that there will be a tremendous wave in robotics over the next 10 to 20 years. A key part of this will be the development of humanoid robots, which he believes will eventually be capable of complex industrial and domestic tasks, interacting with natural language, likely by 2035.
This recent surge in AI capabilities has been heavily driven by advancements in deep learning and the availability of massive computing power. Tesla is making heavy investments in both software and hardware. It recently started construction of its Cortex 2.0 Supercomputer cluster at Giga Texas.
Envisioning Sustainable Abundance & AGI
The conversation also covered the topics of Artificial General Intelligence. Ashok offered a pretty bold prediction that AGI will arrive in as little as the next 10 years, based on the rate of advancement that he’s seen so far. He further projected that AI-based software could become capable of performing most human tasks, whether spreadsheets or even robotic athletics, within the next 15 years.
This technological leap, he believes, ties into Tesla’s newer mission statement of sustainable abundance. Sustainable abundance is where the combination of intelligent machines and effective robotics helps to move greater portions of society away from poverty. This has become Tesla’s guiding philosophy since the 2025 All-Hands Meeting earlier this year.
Sustainable abundance should be a win-win scenario for all involved, helping reshape both production and creative industries to help humans do what they want to do rather than what they have to do.
Future of Mobility
As FSD and other AGI tech mature, Ashok believes that all cars being manufactured by 2035 will become autonomous. By then, the very concept of car ownership may change and transform. Owning a car would be a more “premium experience,” as the convenience and efficiency of self-driving vehicles might make personal ownership less of a necessity for many people. This shift would also necessitate infrastructure improvements to accommodate potentially increased vehicle usage.
We took a look at what the future may look like when autonomous vehicles become commonplace. It’ll have a drastic effect on our society, as parking lots will need to be a fraction of the size they are today, drop-off and loading zones will need to be bigger, and, for the most part, road signs may no longer be needed.
Will need this big time in the future. With autonomous vehicles we'll have affordable premium transport for everyone. This will likely increase traffic due to the increased usage, even though each vehicle is much more efficiently utilized. https://t.co/xvdvmxmzxd
Touching on the Indian vehicle market, Ashok noted that EVs, especially when combined with technologies like FSD, are well suited to the typical travel patterns in India and could make a big difference. With Tesla putting its eyes on a potential factory expansion in the coming years in India, there’s a lot riding on Tesla being able to take on the challenge of Indian roadways, where traffic laws are not enforced and well known.
Ashok’s interview was a fantastic look into what he believes will be next for Tesla - and he left with some parting advice for the next generation of engineers.
Master core concepts and leverage the wealth of online resources available. There is an emphasis on talent and innovation over traditional corporate hierarchies, and don’t forget your priorities: work and family.
You can watch the full interview here. Closed captioning is available.
This morning, Tesla announced the appointment of Jack Hartung, President and former Chief Financial Officer of Chipotle Mexican Grill, to its Board of Directors. Jack will join the board on June 1st of this year and will also serve on Tesla’s Audit Committee.
Hartung brings over two decades of financial leadership experience to Tesla’s board. During his tenure at Chipotle, he held several leadership positions, including President and Chief Strategy Officer, as well as Chief Financial and Administrative Officer. Under his financial stewardship, Chipotle expanded immensely, now operating over 3,700 restaurants worldwide.
In addition to his new role at Tesla, Hartung serves on the Boards of Portillo’s Inc., The Honest Company, Inc., and ZocDoc, Inc.
Interestingly, Hartung has opted to forgo any cash or equity compensation for his role on Tesla’s board.
Tesla’s board now comprises nine members, including Chair Robyn Denholm, CEO Elon Musk, his brother Kimbal Musk, and Airbnb co-founder Joe Gebbia.
Areas of Interest
Hartung joins Tesla as it works through regulatory challenges in Europe around FSD, although it seems like Europe will finally see FSD introduced this September, at least on the highway.
Given Jack’s experience in the food sector, it’ll also be interesting to see whether he offers input on Tesla’s upcoming drive-in diner and whether the company expands the concept beyond a one-off Supercharger. We recently shared exclusive photos of the interior of the project, which appears to be in the final stages of construction.
Hartung’s experience in scaling operations may also be crucial during Tesla’s expansion of the Robotaxi network when it begins expanding outside of Austin, Texas.
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