Matrix headlights have a circular dome projector on the outer edge
Not a Tesla App
Tesla has been on a roll with updates recently, and now update 2024.20 was released to employees over the weekend. This update builds on the many features in the Spring Update and adds a few big improvements.
Adaptive Headlight Support in the US & Canada
Tesla recently tweeted that update 2024.20 is now rolling out to vehicles - and they included some surprises in this tweet. This update includes Adaptive Headlights for the United States and Canada! North America will receive adaptive headlight support by including the curve adaption feature discussed below. South Korea and Puerto Rico will also gain the feature.
Europe and possibly other regions will also include this feature in addition to being able to illuminate further on highways.
Tesla also shed some light on what’s included in the hot weather improvements. All Model 3s and Model Ys, as well as refreshed Model S and X’s will now cool down faster in hot temperatures. They will also better regulate high-voltage battery temperature for improved Supercharging performance. The Cybertruck in particular will also benefit from a quieter AC.
The full list of improvements in update 2024.20 is discussed below.
Update: This article has been updated to make it clearer that the U.S. and Canada did not receive full matrix headlight support. What they received was the ability for vehicles with matrix headlights to light up the sides of the road in response to upcoming turns. The Tesla release notes state:
“Headlights now adapt for curves in the road ahead for greater visibility in low lighting. Enable this feature in your vehicle settings at Lights > Adaptive Headlights.”
This differs from the European release notes for the same feature which state:
“Headlights now adapts for curves in the road ahead of you and illuminate farther when you’re driving on a motorway.”
“Enable these features in your vehicle settings at Lights > Adaptive Headlights.”
Adaptive Headlights
New updates to Adaptive Headlights are arriving for European cars with matrix headlights. The new update allows the headlights to adapt to curves in the road ahead of you, enabling better illumination. Having the adaptive headlights work for curves is the second major update for European headlights. Update 2024.8 added adaptive high-beam support, letting your high beams stay on longer by turning off select LEDs in the headlights.
Update 2024.2 first brought adaptive high beams to the new Model 3, before it was later introduced to older vehicles with matrix headlights. At this time, it’s not clear whether the improvements to headlights around curves will be exclusive to matrix headlights or also support the new Model 3.
How to Tell If You Have Matrix Headlights
How do you know if you have matrix headlights on your Tesla? On the outer edge of the headlight, there will be a large, round projector dome, like in the image below. If there isn’t a dome, those are standard non-matrix headlights. Another way to tell is to run a stock light show while facing a wall. If the Tesla logo, in letters, pops up, you have matrix headlights.
For now, North America still does not have adaptive headlight support, mostly due to legislative and testing issues in the United States. The US recently approved adaptive headlights, and a Tesla employee mentioned they’re working on it. Canada has legalized adaptive headlights since 2018, so we see this deployed in North America at some point in the future.
Supercharger Races on Beach Buggy Racing 2
Tesla is still improving its Arcade functionality, with the addition of local leaderboards at Superchargers in Beach Buggy Racing 2. It appears that each individual Supercharger site will have its own leaderboard, which drivers can compete on while their cars charge. Tesla says there will also be special races to compete in this Beach Buggy Racing 2 update.
Tesla owners can plug in and play with a controller, the touchscreen, or their vehicle’s steering wheel. Thanks to steer-by-wire on the Cybertruck, the actual wheels on the truck won’t move like they do on other Tesla models when playing the game.
We continue to hope that future refreshes to the S, 3, X, and Y will eventually receive steer-by-wire as well, as the feature has quite a few unique uses, whether driving or parked.
Autopilot Strikes and Suspension
An updated Autopilot Strike system, similar to the one that is on Tesla’s upcoming FSD V12.4 update, is on 2024.20 as well. At five strikes, users will be suspended from the use of Autopilot like before, but now Tesla will remove a strike for each 7-day period the driver goes without receiving a strike.
FSD 12.4 also improves vision-based monitoring and removes the steering wheel nag, but that’s not in this latest Tesla update, but will likely be added in the future.
Tesla tends to release new Autopilot features in their FSD updates before releasing them to the wider public for regular Autopilot use.
Hot Weather Improvements
The last set of user-end improvements coming in 2024.20 will be related to hot weather, the opposite of 2024.2.6’s cold weather update. This set of changes intends to improve AUTO mode HVAC performance in hot weather, helping to cool down the cabin faster, while also maintaining comfort at lower noise levels.
There have been several updates in the last six months to Tesla’s HVAC systems, all helping to deliver a quieter, more comfortable experience, with one of the last major ones introducing cool-down or warm-up periods before blowing air into the car cabin.
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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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