After a month-long delay, Tesla sent FSD v12.4.2 to employees yesterday with update 2024.15.10. After just a few hours of the update going out to employees, Tesla started sending it to OG testers as well, who were already on FSD v12.4.1.
Issues Behind Delay
On X, Elon Musk broke down some of the details behind the delays. He mentioned that part of the issues behind the V12.4.2 delays were based on training. Tesla was seeing fewer interventions with FSD v12.4, but the release suffered from driving smoothness, which ironically was supposed to be one of the key features of this release.
Musk explained that part of the issue was due to too much focus on interventions, and not enough on normal driving. He compared it to training a doctor on emergency room patients, versus training on regular preventative care.
Vision-Based Attention Monitoring Changes
With FSD update 12.4.2, Tesla changed some of the language used for its Vision-Based Attention Monitoring in the release notes. Here’s a breakdown of some of the key points in the language. The changes to the release notes are below. Phrases or words that were removed are crossed out, while those added are in bold.
“When Full Self-Driving (Supervised) is enabled, the driver monitoring system now primarily relies on the cabin camera to determine driver attentiveness. This enhancement is available on vehicles equipped with a cabin camera and only when the cabin camera has clear and continuous visibility of the driver's eyes Cabin camera must have clear visibility (e.g., the camera is not occluded, eyes, arms are visible, there is sufficient cabin illumination, and the driver is looking forward at the road ahead and not wearing sunglasses, a hat with a low brimwithout sunglasses, or other objects covering thetheir eyes). Outside of theseIn other circumstances, the driver monitoring system will continue toprimarily rely on a combination of torque-based (steering wheel) and vision-based monitoring to detect driver attentiveness. When the cabin camera is actively monitoring driver attentiveness, a green dot appears next to the steering wheel icon on the touchscreen.
If the cabin camera detects inattentiveness the driver to be inattentive, a warning will appear. The warning can be dismissed by the driver immediately reverting their attention back to the road ahead. Warnings will escalate depending on the nature and frequency of detected inattentiveness, with continuous inattention leading to a Strikeout.
Cabin camera images do not leave the vehicle itself, which means the system cannot save or transmit information unless you enable data sharing.”
Arms Need to be Visible and Other Changes
There are various interesting changes here. While some of the changes are just improved wording to make the feature clearer, there are others that are worth highlighting which could point at changes to Tesla’s vision-based monitoring.
The first is the addition of “arms are visible.” Apparently, Tesla now wants to be able to see your arms to better detect attentiveness. Tesla may want to see that your arms are on the steering wheel, or they maybe they want to make sure your arms are moving so that someone isn’t able to post a static photo in front of the cabin camera to circumvent the attention monitoring. The reason isn’t clear, but looks like having your arms visible is now a requirement.
There are other small changes like the removal of a “hat with a low brim,” which was replaced by more generic wording that says the driver’s eyes must be visible.
However, near the end, Tesla removed the portion that said the driver monitoring system will rely on a combination of torque-based (steering wheel) and vision-based monitoring. This was changed to simply say that Tesla will rely primarily on torque-based (steering wheel) monitoring when vision-based monitoring is unavailable. It’s not clear whether this is just semantics and the wording now better describes how the vision-monitoring feature works, or if Tesla made changes so that the vehicle is simply either doing vision monitoring or steering wheel torque detection, and it doesn’t try to combine the two sources to detect whether the driver is paying attention.
Even more interestingly, the last line that says “cabin camera images do not leave the vehicle itself, which means the system cannot save or transmit information unless you enable data sharing,” has been removed. This could point to Tesla saving images of the cabin camera to improve its AI training model.
Either way, we’re glad to finally see FSD v12.4.2 going out to employees and early-access owners. The release is expected to have far fewer interventions and improve vehicle smoothness during braking and acceleration. If there are no major issues found, this update could go wide to all customers with FSD and on update 2024.14 or lower in the coming weeks.
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As December approaches, Tesla’s highly anticipated Holiday update draws closer. Each year, this eagerly awaited software release transforms Tesla vehicles with new features and festive flair. If you’re not familiar with Tesla’s holiday updates, take a look at what Tesla has launched in the Holiday update the past few years.
For this chapter in our series, we’re dreaming up ways Tesla could improve the charging experience and even add some additional safety features. So let’s take a look.
Destination State of Charge
Today, navigating to a destination is pretty straightforward on your Tesla. Your vehicle will automatically let you know when and where to charge, as well as for how long. However, you’ll likely arrive at your destination at a low state of charge.
Being able to set your destination state of charge would be an absolute game-changer for ease of road-tripping. After all, the best EV to road trip in is a Tesla due to the Supercharger network. It looks like Tesla may be listening. Last week, Tesla updated their app and hinted at such a feature coming to the Tesla app. A Christmas present, maybe?
Battery Precondition Options
While Tesla automatically preconditions your battery when needed for fast charging, there are various situations where manually preconditioning the battery would be beneficial.
Currently, there is no way to precondition for third-party chargers unless you “navigate” to a nearby Supercharger. If you need to navigate to a Supercharger that’s close by, the short distance between your location and the Supercharger will also not allow enough time to warm up the battery, causing slower charging times.
While we already mentioned Live Activities in the Tesla app wishlist, they’d be especially useful while Supercharging. Live Activities are useful for short-term information you want to monitor, especially if it changes often — which makes them perfect for Supercharging, especially if you want to avoid idle fees.
Vehicle-to-Load / Vehicle-to-Home Functionality
The Cybertruck introduced Tesla Power Share, Tesla’s name for Vehicle-to-Home functionality (V2H). V2H allows an EV to supply power directly to a home. By leveraging the vehicle’s battery, V2H can provide backup power during outages and reduce energy costs by using stored energy during peak rates.
Tesla Power Share integrates seamlessly with Tesla Energy products and the Tesla app. We’d love to see this functionality across the entire Tesla lineup. Recently a third party demonstrated that bidirectional charging does work on current Tesla vehicles – namely on a 2022 Model Y.
Adaptive Headlights for North America
While Europe and China have had access to the Adaptive Headlights since earlier this year, North America is still waiting. The good news is that Lars Moravy, VP of Vehicle Engineering, said that these are on their way soon.
Blind Spot Indication with Ambient Lighting
Both the 2024 Highland Model 3 Refresh and the Cybertruck already have ambient lighting features, but they don’t currently offer a practical purpose besides some eye candy. So why not integrate that ambient lighting into the Blindspot Warning system so that the left or right side of the vehicle lights up when there’s a vehicle in your blind spot? Currently, only a simple red dot lights up in the front speaker grill, and the on-screen camera will also appear with a red border when signaling.
Having the ambient lighting change colors when a vehicle is in your blind spot would be a cool use of the technology, especially since the Model Y Juniper Refresh and Models S and X are supposed to get ambient lighting as well.
Tesla’s Holiday update is expected to arrive with update 2024.44.25 in just a few short weeks. We’ll have extensive coverage of its features when it finally arrives, but in the meantime, be sure to check out our other wishlist articles:
It’s time for another dive into how Tesla intends to implement FSD. Once again, a shout out to SETI Park over on X for their excellent coverage of Tesla’s patents.
This time, it's about how Tesla is building a “universal translator” for AI, allowing its FSD or other neural networks to adapt seamlessly to different hardware platforms.
That translating layer can allow a complex neural net—like FSD—to run on pretty much any platform that meets its minimum requirements. This will drastically help reduce training time, adapt to platform-specific constraints, decide faster, and learn faster.
We’ll break down the key points of the patents and make them as understandable as possible. This new patent is likely how Tesla will implement FSD on non-Tesla vehicles, Optimus, and other devices.
Decision Making
Imagine a neural network as a decision-making machine. But building one also requires making a series of decisions about its structure and data processing methods. Think of it like choosing the right ingredients and cooking techniques for a complex recipe. These choices, called "decision points," play a crucial role in how well the neural network performs on a given hardware platform.
To make these decisions automatically, Tesla has developed a system that acts like a "run-while-training" neural net. This ingenious system analyzes the hardware's capabilities and adapts the neural network on the fly, ensuring optimal performance regardless of the platform.
Constraints
Every hardware platform has its limitations – processing power, memory capacity, supported instructions, and so on. These limitations act as "constraints" that dictate how the neural network can be configured. Think of it like trying to bake a cake in a kitchen with a small oven and limited counter space. You need to adjust your recipe and techniques to fit the constraints of your kitchen or tools.
Tesla's system automatically identifies these constraints, ensuring the neural network can operate within the boundaries of the hardware. This means FSD could potentially be transferred from one vehicle to another and adapt quickly to the new environment.
Let’s break down some of the key decision points and constraints involved:
Data Layout: Neural networks process vast amounts of data. How this data is organized in memory (the "data layout") significantly impacts performance. Different hardware platforms may favor different layouts. For example, some might be more efficient with data organized in the NCHW format (batch, channels, height, width), while others might prefer NHWC (batch, height, width, channels). Tesla's system automatically selects the optimal layout for the target hardware.
Algorithm Selection: Many algorithms can be used for operations within a neural network, such as convolution, which is essential for image processing. Some algorithms, like the Winograd convolution, are faster but may require specific hardware support. Others, like Fast Fourier Transform (FFT) convolution, are more versatile but might be slower. Tesla's system intelligently chooses the best algorithm based on the hardware's capabilities.
Hardware Acceleration: Modern hardware often includes specialized processors designed to accelerate neural network operations. These include Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). Tesla's system identifies and utilizes these accelerators, maximizing performance on the given platform.
Satisfiability
To find the best configuration for a given platform, Tesla employs a "satisfiability solver." This powerful tool, specifically a Satisfiability Modulo Theories (SMT) solver, acts like a sophisticated puzzle-solving engine. It takes the neural network's requirements and the hardware's limitations, expressed as logical formulas, and searches for a solution that satisfies all constraints. Try thinking of it as putting the puzzle pieces together after the borders (constraints) have been established.
Here's how it works, step-by-step:
Define the Problem: The system translates the neural network's needs and the hardware's constraints into a set of logical statements. For example, "the data layout must be NHWC" or "the convolution algorithm must be supported by the GPU."
Search for Solutions: The SMT solver explores the vast space of possible configurations, using logical deduction to eliminate invalid options. It systematically tries different combinations of settings, like adjusting the data layout, selecting algorithms, and enabling hardware acceleration.
Find Valid Configurations: The solver identifies configurations that satisfy all the constraints. These are potential solutions to the "puzzle" of running the neural network efficiently on the given hardware.
Optimization
Finding a working configuration is one thing, but finding the best configuration is the real challenge. This involves optimizing for various performance metrics, such as:
Inference Speed: How quickly the network processes data and makes decisions. This is crucial for real-time applications like FSD.
Power Consumption: The amount of energy used by the network. Optimizing power consumption is essential for extending battery life in electric vehicles and robots.
Memory Usage: The amount of memory required to store the network and its data. Minimizing memory usage is especially important for resource-constrained devices.
Accuracy: Ensuring the network maintains or improves its accuracy on the new platform is paramount for safety and reliability.
Tesla's system evaluates candidate configurations based on these metrics, selecting the one that delivers the best overall performance.
Translation Layer vs Satisfiability Solver
It's important to distinguish between the "translation layer" and the satisfiability solver. The translation layer is the overarching system that manages the entire adaptation process. It includes components that analyze the hardware, define the constraints, and invoke the SMT solver. The solver is a specific tool used by the translation layer to find valid configurations. Think of the translation layer as the conductor of an orchestra and the SMT solver as one of the instruments playing a crucial role in the symphony of AI adaptation.
Simple Terms
Imagine you have a complex recipe (the neural network) and want to cook it in different kitchens (hardware platforms). Some kitchens have a gas stove, others electric; some have a large oven, others a small one. Tesla's system acts like a master chef, adjusting the recipe and techniques to work best in each kitchen, ensuring a delicious meal (efficient AI) no matter the cooking environment.
What Does This Mean?
Now, let’s wrap this all up and put it into context—what does it mean for Tesla? There’s quite a lot, in fact. It means that Tesla is building a translation layer that will be able to adapt FSD for any platform, as long as it meets the minimum constraints.
That means Tesla will be able to rapidly accelerate the deployment of FSD on new platforms while also finding the ideal configurations to maximize both decision-making speed and power efficiency across that range of platforms.
Putting it all together, Tesla is preparing to license FSD, Which is an exciting future. And not just on vehicles - remember that Tesla’s humanoid robot - Optimus - also runs on FSD. FSD itself may be an extremely adaptable vision-based AI.
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