Tesla has released FSD Beta 11.4 to employees, with original testers hopefully soon to follow. The update is version 2023.6.15, making it the first time FSD Beta has been based on a 2023 branch. With this update, Tesla has prioritized refining the vehicle's performance over introducing new features.
Enhanced Safety Features: Pedestrians and Vulnerable Road Users
Some standout improvements include enhanced pedestrian recognition and response, allowing Tesla vehicles to predict pedestrian movements better and react accordingly. This leads to a smoother driving experience at intersections, as vehicles can accurately anticipate when to stop or proceed.
Another critical aspect of the update is its improved responsiveness to vulnerable road users, such as bikers and cyclists. The FSD Beta 11.4 update now reacts more quickly to these road users and adjusts the vehicle's speed when necessary, ensuring a safer experience for everyone involved.
Improved Driving Performance: Urban, Rural, and Adverse Weather Conditions
One of the most notable features of the update is its weather-adaptive speed control. The FSD Beta 11.4 adjusts the maximum Autopilot speed based on current weather conditions, considering factors such as visibility, road wetness, tire tread, and even tire spray from other vehicles, ensuring a safer driving experience in adverse conditions.
Urban drivers will appreciate the optimized turn performance in city environments, as the update enables Tesla vehicles to navigate dense areas and challenging turns more effectively. This results in smoother navigation while avoiding obstacles like parked cars and bus lanes. Additionally, the update brings advanced bus lane recognition, helping prevent the vehicle from accidentally entering restricted areas.
In rural areas, the FSD Beta 11.4 should improve, with upgraded lane, line, and road edge detection. This enhancement is beneficial when driving on roads with inconsistent markings and paving, providing a safer experience. Alongside these improvements, the update also refines lane guidance and partial cut-in predictions. By leveraging Tesla's extensive library of clips and auto-labelling system, these enhancements ensure the vehicle remains in the correct lane and accurately predicts the movement of other vehicles.
Smarter Navigation: Lane Guidance and Speed Adjustments
The FSD Beta 11.4 also considers upcoming navigation deadlines, adjusting the vehicle's speed accordingly during lane changes, resulting in refined lane change speed control. Furthermore, Tesla's new Vision Speed network allows its cars to infer the typical driving speed on any given roadway, optimizing speed control in various situations, such as residential areas and parking lots.
Finally, Tesla has enhanced long-range path blockage detection, enabling vehicles to merge into other lanes more swiftly to avoid obstacles on the road.
Who is Eligible for This Beta
With the latest FSD Beta being based on Tesla's 2023.6 code branch, this will make FSD Beta available to many new owners who are on a 2023.2 or 2023.6 update. So if you've been waiting to be eligible for FSD Beta before subscribing, you may soon get your chance. However, it may still take several weeks before this update, or a revision of it goes into wide release, so don't click that button just yet.
Release Date
Tesla will typically release an update to employees and then to OG testers before having it go out to everyone else in waves. However, if Tesla discovers a blocking issue, the rollout is halted until an update becomes available. Once a revision is available, Tesla will restart the rollout with employees again.
FSD Beta 11.3 went through various revisions before finally being available to most owners. However, that was a much larger update, so FSD Beta 11.4 will likely see fewer revisions. In the past we've typically seen 1 to 3 revisions before a major update to FSD Beta went into 'wide' release.
What happens next will depend on whether Tesla encounters any major issues. Tesla may release FSD Beta 11.4 to OG testers or it may be quiet for several days before 11.4.1 or a similar revision to FSD Beta surfaces.
FSD Beta 11.4 demonstrates Tesla's unwavering commitment to pushing the boundaries of autonomous driving technology, bringing us closer to a future where fully autonomous vehicles are the norm. Take a look at the release notes for FSD Beta 11.4 for a thorough look at all the changes in this update.
Ordering a New Tesla?
Consider using our referral code (nuno84363) to get up to $2,000 off your new Tesla and get 3 Months of FSD for free.
Tesla launched two FSD updates simultaneously on Saturday night, and what’s most interesting is that they arrived on the same software version. We’ll dig into that a little later, but for now, there’s good news for everyone. For Hardware 3 owners, FSD V12.6.1 is launching to all vehicles, including the Model 3 and Model Y. For AI4 owners, FSD V13.2.4 is launching, starting with the Cybertruck.
FSD V13.2.4
A new V13 build is now rolling out to the Cybertruck and is expected to arrive for the rest of the AI4 fleet soon. However, this build seems to be focused on bug fixes. There are no changes to the release notes for the Cybertruck with this release, and it’s unlikely to feature any changes when it arrives on other vehicles.
FSD V12.6.1 builds upon V12.6, which is the latest FSD version for HW3 vehicles. While FSD V12.6 was only released for the redesigned Model S and Model X with HW3, FSD V12.6.1 is adding support for the Model 3 and Model Y.
While this is only a bug-fix release for users coming from FSD V12.6, it includes massive improvements for anyone coming from an older FSD version. Two of the biggest changes are the new end-to-end highway stack that now utilizes FSD V12 for highway driving and a redesigned controller that allows FSD to drive “V13” smooth.
It also adds speed profiles, earlier lane changes, and more. You can read our in-depth look at all the changes in FSD V12.6.
Same Update, Multiple FSD Builds
What’s interesting about this software version is that it “includes" two FSD updates, V12.6.1 for HW3 and V13.2.4 for HW4 vehicles. While this is interesting, it’s less special when you understand what’s happening under the hood.
The vehicle’s firmware and Autopilot firmware are actually completely separate. While a vehicle downloading a firmware update may look like a singular process, it’s actually performing several functions during this period. First, it downloads the vehicle’s firmware. Upon unpacking the update, it’s instructed which Autopilot/FSD firmware should be downloaded.
While the FSD firmware is separate, the vehicle can’t download any FSD update. The FSD version is hard-coded in the vehicle’s firmware that was just downloaded. This helps Tesla keep the infotainment and Autopilot firmware tightly coupled, leading to fewer issues.
What we’re seeing here is that HW3 vehicles are being told to download one FSD version, while HW4 vehicles are being told to download a different version.
While this is the first time Tesla has had two FSD versions tied to the same vehicle software version, the process hasn’t actually changed, and what we’re seeing won’t lead to faster FSD updates or the ability to download FSD separately. What we’re seeing is the direct result of the divergence of HW3 and HW4.
While HW3/4 remained basically on the same FSD version until recently, it is now necessary to deploy different versions for the two platforms. We expect this to be the norm going forward, where HW3 will be on a much different version of FSD than HW4. While each update may not include two different FSD versions going forward, we may see it occasionally, depending on which features Autopilot is dependent on.
Thanks to Greentheonly for helping us understand what happened with this release and for the insight into Tesla’s processes.
At the 2025 Consumer Electronics Show, Nvidia showed off its new consumer graphics cards, home-scale compute machines, and commercial AI offerings. One of these offerings included the new Nvidia Cosmos training system.
Nvidia is a close partner of Tesla - in fact, they produce and supply the GPUs that Tesla uses to train FSD - the H100s and soon-to-be H200s, located at the new Cortex Supercomputing Cluster at Giga Texas. Nvidia will also challenge Tesla’s lead in developing and deploying synthetic training data for an autonomous driving system - something Tesla is already doing.
However, this is far more important for other manufacturers. We’re going to take a look at what Nvidia is offering and how it compares to what Tesla is already doing. We’ve done a few deep dives into how Tesla’s FSD works, how Tesla streamlines FSD, and, more recently, how they optimize FSD. If you want to get familiar with a bit of the lingo and the background knowledge, we recommend reading those articles before continuing, but we’ll do our best to explain how all this synthetic data works.
Nvidia Cosmos
Nvidia’s Cosmos is a generative AI model created to accelerate the development of physical AI systems, including robots and autonomous vehicles. Remember - Tesla’s FSD is also the same software that powers their humanoid robot, Optimus. Nvidia is aiming to tackle physical, real-world deployments of AI anywhere from your home, your street, or your workplace, just like Tesla.
Cosmos is a physics-aware engine that learns from real-world video and builds simulated video inputs. It tokenizes data to help AI systems learn quicker, all based on the video that is input into the system. Sound familiar? That’s exactly how FSD learns as well.
Cosmos also has the capability to do sensor-fused simulations. That means it can take multiple input sources - video, LiDAR, audio, or whatever else the user intends, and fuse them together into a single-world simulation for your AI model to learn from. This helps train, test, and validate autonomous vehicle behavior in a safe, synthetic format while also providing a massive breadth of data.
Data Scaling
Of course, Cosmos itself still requires video input - the more video you feed it, the more simulations it can generate and run. Data scaling is a necessity for AI applications, as you’ll need to feed it an infinite amount of data to build an infinite amount of scenarios for it to train itself on.
Synthetic data also has a problem - is it real? Can it predict real-world situations? In early 2024, Elon Musk commented on this problem, noting that data scales infinitely both in the real world and in simulated data. A better way to gather testing data is through real-world data. After all, no AI can predict the real world just yet - in fact, that’s an excellent quantum computing problem that the brightest minds are working on.
Yun-Ta Tsai, an engineer at Tesla’s AI team, also mentioned that writing code or generating scenarios doesn’t cover what even the wildest AI hallucinations might come up with. There are lots of optical phenomena and real-world situations that don’t necessarily make sense in the rigid training sets that AI would develop, so real-world data is absolutely essential to build a system that can actually train a useful real-world AI.
Tesla has billions of miles of real-world video that can be used for training, according to Tesla’s Social Media Team Lead Viv. This much data is essential because even today, FSD encounters “edge cases” that can confuse it, slow it down, or render it incapable of continuing, throwing up the dreaded red hands telling the user to take over.
Cosmos was trained on approximately 20 million hours of footage, including human activities like walking and manipulating objects. On the other hand, Tesla’s fleet gathers approximately 2,380 recorded minutes of real-world video per minute. Every 140 hours - just shy of 6 days - Tesla’s fleet gathers 20 million hours of footage. That was a little bit of back-of-the-napkin math, calculated at 60 mph as the average speed.
Generative Worlds
Both Tesla’s FSD and Nvidia’s Cosmos can generate highly realistic, physics-based worlds. These worlds are life-like environments and simulate the movement of people and traffic and the real-life position of obstacles and objects, including curbs, fences, buildings, and other objects.
Tesla uses a combination of real-world data and synthetic data, but the combination of data is heavily weighted to real-world data. Meanwhile, companies who use Cosmos will be weighting their data heavily towards synthetically created situations, drastically limiting what kind of cases they may see in their training datasets.
As such, while generative worlds may be useful to validate an AI quickly, we would argue that these worlds aren’t as useful as real-world data to do the training of an AI.
Overall, Cosmos is an exciting step - others are clearly following in Tesla’s footsteps, but they’re extremely far behind in real-world data. Tesla has built a massive first-mover advantage in AI and autonomy, and others are now playing catch-up.
We’re excited to see how Tesla’s future deployment of its Dojo Supercomputer for Data Labelling adds to its pre-existing lead, and how Cortex will be able to expand, as well as what competitors are going to be bringing to the table. After all, competition breeds innovation - and that’s how Tesla innovated in the EV space to begin with.
