Inside Tesla’s FSD: Patent Explains How FSD Works

By Karan Singh
Not a Tesla App

Thanks to a Tesla patent published last year, we have a great look into how FSD operates and the various systems it uses. SETI Park, who examines and writes about patents, also highlighted this one on X.

This patent breaks down the core technology used in Tesla’s FSD and gives us a great understanding of how FSD processes and analyzes data.

To make this easily understandable, we’ll divide it up into sections and break down how each section impacts FSD.

Vision-Based

First, this patent describes a vision-only system—just like Tesla’s goal—to enable vehicles to see, understand, and interact with the world around them. The system describes multiple cameras, some with overlapping coverage, that capture a 360-degree view around the vehicle, mimicking but bettering the human equivalent.

What’s most interesting is that the system quickly and rapidly adapts to the various focal lengths and perspectives of the different cameras around the vehicle. It then combines all this to build a cohesive picture—but we’ll get to that part shortly.

Branching

The system is divided into two parts - one for Vulnerable Road Users, or VRUs, and the other for everything else that doesn’t fall into that category. That’s a pretty simple divide - VRUs are defined as pedestrians, cyclists, baby carriages, skateboarders, animals, essentially anything that can get hurt. The non-VRU branch focuses on everything else, so cars, emergency vehicles, traffic cones, debris, etc. 

Splitting it into two branches enables FSD to look for, analyze, and then prioritize certain things. Essentially, VRUs are prioritized over other objects throughout the Virtual Camera system.

The many data streams and how they're processed.
The many data streams and how they're processed.
Not a Tesla App

Virtual Camera

Tesla processes all of that raw imagery, feeds it into the VRU and non-VRU branches, and picks out only the key and essential information, which is used for object detection and classification.

The system then draws these objects on a 3D plane and creates “virtual cameras” at varying heights. Think of a virtual camera as a real camera you’d use to shoot a movie. It allows you to see the scene from a certain perspective.

The VRU branch uses its virtual camera at human height, which enables a better understanding of VRU behavior. This is probably due to the fact that there’s a lot more data at human height than from above or any other angle. Meanwhile, the non-VRU branch raises it above that height, enabling it to see over and around obstacles, thereby allowing for a wider view of traffic.

This effectively provides two forms of input for FSD to analyze—one at the pedestrian level and one from a wider view of the road around it.

3D Mapping

Now, all this data has to be combined. These two virtual cameras are synced - and all their information and understanding are fed back into the system to keep an accurate 3D map of what’s happening around the vehicle. 

And it's not just the cameras. The Virtual Camera system and 3D mapping work together with the car’s other sensors to incorporate movement data—speed and acceleration—into the analysis and production of the 3D map.

This system is best understood by the FSD visualization displayed on the screen. It picks up and tracks many moving cars and pedestrians at once, but what we see is only a fraction of all the information it’s tracking. Think of each object as having a list of properties that isn’t displayed on the screen. For example, a pedestrian may have properties that can be accessed by the system that state how far away it is, which direction it’s moving, and how fast it’s going.

Other moving objects, such as vehicles, may have additional properties, such as their width, height, speed, direction, planned path, and more. Even non-VRU objects will contain properties, such as the road, which would have its width, speed limit, and more determined based on AI and map data.

The vehicle itself has its own set of properties, such as speed, width, length, planned path, etc. When you combine everything, you end up with a great understanding of the surrounding environment and how best to navigate it.

The Virtual Mapping of the VRU branch.
The Virtual Mapping of the VRU branch.
Not a Tesla App

Temporal Indexing

Tesla calls this feature Temporal Indexing. In layman’s terms, this is how the vision system analyzes images over time and then keeps track of them. This means that things aren’t a single temporal snapshot but a series of them that allow FSD to understand how objects are moving. This enables object path prediction and also allows FSD to understand where vehicles or objects might be, even if it doesn’t have a direct vision of them.

This temporal indexing is done through “Video Modules”, which are the actual “brains” that analyze the sequences of images, tracking them over time and estimating their velocities and future paths.

Once again, heavy traffic and the FSD visualization, which keeps track of many vehicles in lanes around you—even those not in your direct line of sight—are excellent examples.

End-to-End

Finally, the patent also mentions that the entire system, from front to back, can be - and is - trained together. This training approach, which now includes end-to-end AI, optimizes overall system performance by letting each individual component learn how to interact with other components in the system.

How everything comes together.
How everything comes together.
Not a Tesla App

Summary

Essentially, Tesla sees FSD as a brain, and the cameras are its eyes. It has a memory, and that memory enables it to categorize and analyze what it sees. It can keep track of a wide array of objects and properties to predict their movements and determine a path around them. This is a lot like how humans operate, except FSD can track unlimited objects and determine their properties like speed and size much more accurately. On top of that, it can do it faster than a human and in all directions at once.

FSD and its vision-based camera system essentially create a 3D live map of the road that is constantly and consistently updated and used to make decisions.

Tesla FSD in Europe: June Update

By Karan Singh
Not a Tesla App

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.

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 Launches 'TeslaVision' Contest With Big Prizes — See Last Year’s Winner [VIDEO]

By Karan Singh
Not a Tesla App

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.

The Contest

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.

Hopefully, these winners will also have the opportunity to experience the Robotaxi network while they’re in Austin, as it’s expected to be opened to the public later this month.

Project Loveday

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.

You can find the official submission form and all region-specific details on Tesla's website.

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