Tesla has added a mini player to the Model 3 and Model Y
Teslamaniacs
Tesla's holiday update, which is update 2022.44.25.1, is jammed full of goodies, many of which require a second look. There are significant changes for Model 3 and Model Y owners. In addition, some features drivers use to operate daily, have changed location, size, and functionality.
Music Player
This one may take some getting used to. There is now a new mini-player that splits the music area into two sections. A smaller module for media controls is on the bottom left of the screen. This allows the most used buttons to be closer to the driver.
Users can swipe up on the mini media player to reveal sources, including Apple Music, Spotify, Bluetooth etc. Once a source is selected, the details open up on the right-hand side of the screen for users to select songs or playlists.
The mini media player also allows users to scroll to recent and favorites, settings, and a search function can also be found in this small, reconfigured player. The mini-player will automatically collapse after a few seconds if left expanded.
In addition, Tesla has reintroduced cards that can be found by swiping on the media player in the lower left corner. Cards allow you to view your odometer, trip meter and tire pressure. You can see the new UI cards in action which we took an in-depth look at earlier this week.
Media Player in the Center
Unfortunately, the media player can not be placed back in its previous location. At least, not most of the time. If you're confused, you're not the only one. Here's a breakdown of what's possible. If the music panel is expanded to reveal your song selection, the media controls can be placed on top of that panel, but only when the mini player is hidden.
If the mini player is visible, then the song selection panel on the right will only display your songs/playlists, which does create some additional vertical room to display songs and or album art. However, there is no way to have just the media controls on the right side in a minimal sort of fashion as you could before.
Your choices for media controls are now one of three on the Model 3 and Model Y. You can have the mini player on the left side, which is essentially the new default. You can have the media controls on the right side, but only when the music selection panel is fully expanded (covering the map) and the mini player is hidden, or you can have the media controls hidden completely. If your music controls are hidden, a gray music icon appears in your launcher to bring them back up again.
This can all be a little confusing, and it'd be nice to see Tesla introduce a simple toggle to have the media controls on the left or right.
If you're viewing your tire pressure or odometer in the new UI cards, the media player will also jump back to the center area.
Navigation UI
Tesla has improved the layout of the navigation information
Teslamaniacs
Tesla has redesigned the navigation user interface (UI) layout, which improves the placement of directions, such as the next turn and other available options. Tesla has split the nav UI into two pieces. At the top of the screen will be critical information about the route, including next-turn directions.
The rest of the information, previously found at the top of the screen, is moved to the bottom. These details include travel time, destination details, and options to alter or cancel your navigation.
Both of these modules can be expanded to show more information. For example, swiping down on the top module will reveal additional turns along your route, while swiping up on the bottom module, or tapping the three dots will bring up additional route options such as adding a stop or editing your navigation preferences.
Your current location/town has also been shifted slightly. It used to be placed at the bottom center of the screen, but it's now been shifted slightly and is at the far corner of the display.
Fan Controls
You can now tap to change the fan speed and remain in Auto
Teslamaniacs
When your climate system is set to Auto, Tesla now uses a 'LO', 'MED', and 'HI' fan speed, instead of the previous 1-10 scale. The slider is now gone and replaced with buttons to decrease or increase the fan speed.
Tesla will no longer automatically turn off the Auto climate when you adjust the fan intensity. Even when the fan speed is modified, the HVAC system will remain in Auto.
Manual Mode
You can still use fan speeds 1-10 in manual mode
Walgermo/Twitter
If you turn off Auto on your climate system, you'll still have granular access to the fan speed controls with the old 1-10 scale. You now also have the option to tap or use a slider to adjust the fan speed. A new slider will appear above the climate section, or you can tap the arrows to adjust the fan speed just like you do in Auto.
HomeLink
Tesla has made improvements to the HomeLink UI
Walgermo/Twitter
Tesla has made improvements to HomeLink as well. When near your home location, the HomeLink icon will now display in the status bar at the top of the screen with an 'Activate' or 'Cancel' button.
The HomeLink dropdown will automatically disappear if you shift into reverse, but the option to activate your garage door will remain.
Furthermore, if you have auto-open or auto-close enabled, the status bar will now display the number of feet or meters before the garage door is opened or closed.
Video of UI Updates
There are many more additions and changes in the latest update, but these will impact the user the most. Tesla owners usually love to drive and are accustomed to seeing information displayed the same way in the exact location on the screen. So be sure to give yourself a little extra time to familiarize yourself with the new layout.
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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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