Tesla's latest FSD Beta adds new features and visualizations
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Tesla’s CEO, Elon Musk, tweeted that the latest Full Self-Driving Beta is “a major step change improvement. Still needs another point release of polish before rolling out wide.” That said, most of those who have taken v11.3.1 for a ride are impressed.
There are a number of options that have been added and removed with this beta, as well as new visualizations.
New Visualizations
Let’s start with the bright shiny objects - new visualizations. Anyone who has watched the ride-along on YouTube has noticed that FSD v11.3.1 looks different.
Wider Path
Firstly, the path the vehicle is taking is a much thicker line, representing the entire space the car currently occupies and will occupy in the immediate future.
Chevrons
Chevrons will also appear in the path to show the car slowing down. The opacity and speed at which the chevrons are moving change depending on how quickly the car is slowing.
Stopping Line
A white or black line will now appear in front of the Tesla to tell the supervising driver exactly where the car will stop. There’s no more guessing where the car will stop moving. This line appears as soon as the car realizes that it will have to stop, like when approaching an intersection.
Traffic Lights
The traffic lights will now sometimes appear in blue. The blue light indicates that the vehicle is obeying that traffic light. Let’s say you are turning left at an intersection with three traffic lights present; the light in front of the left lane will be blue, while the other two for traffic going straight will remain gray.
Many have observed more detailed images of the surroundings as well. This includes a rolling tire that appeared while Chuck Cook was out for a ride. As documented in the release notes, the system identifies objects differently and seemingly much more accurately.
FSD Messages
FSD Beta 11.3.1 will now display messages letting you know what the vehicle is doing
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How many times have you enabled FSD, and you or your passengers asked, "what is it doing?" Well, now it tells you exactly what it is doing. For example, if it spots an intersection and a red light, it will say, "stopping for the red light." It will display various messages that make it clear what the vehicle is stopping or waiting for such as stop signs, crossing pedestrians, bicyclists and more. This addition gives us a better look into what the software is doing.
Voice Drive-Notes
This is a significant addition because the software now allows for instant driver feedback. If you must take control away from FSD, a prompt will ask you to explain what happened. You can leave an audio recording. For example, if the software says it is stopping for a red light, but you see the light is green, you can take over and tell Tesla what happened so their team can review it.
Glowing Speed Limit Sign
FSD Beta 11.3.1 will glow the speed limit sign when you exceed the detected speed limit by more than 50%
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The speed limit sign appears in the same location, but now it will glow when the vehicle's speed exceeds the posted limit by more than 50%, for example, going 75 mph in a 50 mph zone.
Speed Limit Offset
Speaking of speed, the option to set your Autopilot speed to an absolute value over the speed limit has been removed. You used to be able to set Autopilot to travel 10 mph over the posted speed limit, which would apply in all cases. However, you'll now have to use the 'relative' option, which lets you choose a percentage value to travel above the limit. So choosing a 15% offset will let the vehicle travel at about 35 mph in a 30 zone, while in a 65 mph zone, the vehicle will travel up to 75 mph.
New 'Minimal Lane Changes' Option
There have been a lot of comments about a smoother drive experience with better, less robotic lane changes. However, if you'd like to reduce the number of lane changes the vehicle makes, there is a new option called 'Minimal Lane Changes.' This new option in the Autopilot menu reduces lane changes by having the vehicle only make a lane change when it's required to follow the route. It prevents the vehicle from making lane changes due to slow-moving traffic in its lane.
New Shortcut for Autopilot Options
FSD Beta 11.3.1 includes a shortcut to the FSD Profiles
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With this beta, Tesla also introduced a new shortcut for some Autopilot options. When tilting the right scroll wheel left/right in previous betas, the vehicle would adjust its follow distance from 2 to 7. This option has now been removed since FSD Beta now handles the follow distance automatically on the highway.
When tilting the scroll wheel, you'll now be presented with a new set of options. They'll allow you to adjust the vehicle's FSD Profile, between Chill, Average and Assertive. This isn't new to this beta, but the ability to quickly change the FSD Profile is. In addition to being able to switch the FSD Profile, you'll also have the option to quickly enable 'Minimal Lane Changes' with this shortcut. The option will only stay enabled for the current drive. The informational box in the vehicle explains how Minimal Lane Changes works:
"When enabled, Full Self-Driving (Beta) will limit changes only to follow the navigation route or when required for safety. This option gets reset when the car is parked at the end of a drive."
Removal of 'Navigate on Autopilot'
With the introduction of FSD Beta to highway driving, Tesla has now removed the 'Navigate on Autopilot' (NoA) feature. NoA was Tesla's term for the vehicle performing lane changes to follow your route. However, with the introduction of FSD Beta v11, this option became redundant and it has now been removed with this update.
Correction: Navigate on Autopilot is still available if you switch to 'Autosteer' on the Autopilot menu. The Navigate on Autopilot options disappear when you're using FSD Beta.
New Autopilot Menu
Tesla has reorganized the Autopilot menu
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With this update, Tesla has redesigned the Autopilot menu to remove older options and make your options clearer.
At the very top Tesla now gives owners three options for Autopilot features, they are, Traffic-Aware Cruise Control (TACC), Autosteer, or FSD Beta.
Autosteer is essentially Basic Autopilot, which will keep the vehicle's speed, brake, keep a proper follow distance and steer within the vehicle's lane.
FSD Beta is the option you'll want if you want the vehicle to make lane changes to follow your route.
After that, you have more FSD options, such as FSD Profile, followed by 'Lane Change Notification,' which was previously a part of Navigate on Autopilot and Speed Offset.
Tesla also cleaned up some other options, removing the toggles for 'Traffic Light and Stop Sign Control' and 'Full Self-Driving Visualization Preview'.
Underneath the Autopilot preferences, you then have Summon and the other usual options.
View Cabin Camera
Plus, we previously reported this undocumented feature that appeared in the first update of 2023. Surprisingly, the preview cabin camera video is also available with this update. This function, found under service, allows owners to view their cabin camera inside the vehicle. That provides some peace of mind for users so they can have an idea of what the camera can and cannot see when activated. If you have any privacy concerns, check out our article on Tesla’s Privacy Policy.
Tesla's FSD Beta v11.3.1 is an impressive step forward, with new visualizations and features that enhance the driving experience. Tesla continues to push the boundaries of self-driving technology, and the positive feedback from early users is a testament to their progress. With the upcoming wide release of v11.3.2, we can only expect more exciting updates and improvements from Tesla. Stay tuned for more updates on the future of autonomous driving.
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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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