Tesla FSD Beta v11.3 Improvements Explained in Plain English

Tesla's FSD Beta 11.3 is now in the hands of Tesla employees

@winnersechelon

Finding a more anticipated Tesla update would be hard than FSD Beta v11.3. We had been waiting for it since AI Day on September 30, 2022. Elon Musk has also done a great job teasing Tesla owners with updates and timelines. The update is now being tested by Tesla employees, leading to some leaks and our first peek into the update. However, Musk has already stated that it will be v11.3.2 that goes to the broader subscriber base, leading us to believe that there will be plenty of tweaks to make after the initial rollout. That said, there is a lot to go through with the latest release notes.

Thanks to Dr. Know-it-all's excellent video (below), here is the breakdown and explanation of what we can expect to see when the newest update beams into Teslas in the United States and Canada.

Single Stack Sensation

Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old...

The single stack is here. In computer architecture and tech terminology, a tech stack is the technologies and systems used for a given system. In this case, Tesla combines vision and planning stack on and off the highway. According to Tesla, the legacy highway stack, which is four years old, relies on several single-camera and single-frame networks and was set up to handle simple lane-specific maneuvers.

By simple maneuvers, the company refers to merging on and off the highway and changing lanes. However, FSD is now able to do so much more. Although Tesla has made considerable strides in the past four years, the latest FSD Beta uses "multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making." In translation, we should see much smoother and less unnatural robotic driving. For example, the Autopilot highway lane change that predictably waited a few seconds before moving over will act much more intuitively with the FSD programming.  

Dr. Know-it-all speculates that the legacy stack controlling highway driving had become so reliable that the company had a high bar to beat when rolling out something new. This change needs to be kept in mind for those who have used FSD a lot or for a long time during highway driving. After you are updated, your car will behave slightly differently the next time you merge onto the highway.   

Voice Driver Notes

Added voice drive-notes. After an intervention, you can now send Tesla an anonymous voice message describing your experience to help improve Autopilot.

If the driver needs to intervene, this message will appear: "Autopilot Disengaged. What Happened? Press voice button to send Tesla an anonymous message describing your experience."

There used to be a button on the screen that you could tap to provide feedback (and it's still available for early testers); however, all you could do was tap it, and that would signal a negative experience, but there was no way to explain what happened. This new feature should assist Tesla engineers in watching the video and listening to the driver's feedback to understand the situation better. It is expected the audio feedback will be converted to text to keep the driver anonymous and let engineers search and read messages.

Expanded Automatic Emergency Braking (AEB)

Expanded Automatic Emergency Braking (AEB) to handle vehicles that cross ego's path. This includes cases where other vehicles run their red light or turn across ego's path, stealing the right-of-way. Replay of previous collisions of this type suggests that 49% of the events would be mitigated by the new behavior. This improvement is now active in both manual driving and autopilot operation.

AEB has been around since the mid-2000s. The system applies brakes if it detects the vehicle in front is slowing down or may have suddenly hit the brakes. Tesla's expanded version of this system will monitor not just the traffic directly ahead but also the sides (cars running red lights) or anything that is "stealing the right-of-way." The company says that nearly half of the collisions of this nature would be avoided with this newly expanded system. Better yet, this is active in Full-Self Driving and manual operation — just another Tesla safety improvement.

Improved Autopilot Reaction Time

Improved autopilot reaction time to red light runners and stop sign runners by 500ms, by increased reliance on object's instantaneous kinematics along with trajectory estimates.

Tesla has improved its Autopilot reaction by 500 milliseconds or half a second. It doesn't sound like much, but this system mainly calculates how to respond to drivers running stop signs or red lights. For example, let's say you are driving through your neighborhood at 25 miles per hour and approaching an intersection where you have the right of way. Suddenly a car appears, and the collision has already happened when you realize it is not stopping. By calculating an object's instantaneous kinematics along with trajectory estimates, Telsa would respond in just about the same time as a blink of an eye. At 25 mph, your car is moving at 36.6 feet (11 meters) per second. Imagine what an extra 17.3 feet (5.5 metres) would do in this situation. It's likely the difference between a collision and a near crash.

Dr. Know-it-all Explains the Release Notes

Overall Driving Advancements

Improved handling through high speed and high curvature scenarios by offsetting towards inner lane lines.

Another improvement involves offsetting the vehicle towards the inner lane lines during a turn rather than keeping it dead center in the lane. This biasing towards the inside of the arc is a more natural trajectory for drivers and will help them avoid getting too close to vehicles coming from the other direction. 

Improved longitudinal control response smoothness when following lead vehicles by better modeling the possible effect of lead vehicles’ brake lights on their future speed profiles.

Tesla's AI team has been working on better modeling the possible effect of lead vehicles' brake lights on their future speed profiles. Previously, the Tesla would ignore the brake lights until it was too late, resulting in an uncomfortable situation where the car would have to brake abruptly to avoid hitting the vehicle in front of it. However, Tesla's new modeling approach will enable it to react sooner and more smoothly to brake lights by predicting the lead vehicle's trajectory and speed. This improvement is not safety-critical but will make users more comfortable and provide a better driving experience. 

Improved recall for close-by cut-in cases by 20% by adding 40k autolabeled fleet clips of this scenario to the dataset. Also improved handling of cut-in cases by improved modeling of their motion into ego's lane, leveraging the same for smoother lateral and longitudinal control for cut-in objects.

Recall is all about false negatives, which means the car may overreact to a situation, perhaps slamming on the brakes when someone cuts in front instead of slowing down. This update has improved recall by 20% for close-by cut-in cases, the polite way of saying being cut off. Telsa autolabeled 40,000 clips of this scenario to the dataset, which should reduce false negatives. However, the car will also handle being cut off with more control. If slamming on the brakes is not required, gradual slowing is likely what the Tesla will do.

FSD Can Better Recognize Buses

Improved semantic detections for school busses by 12% and vehicles transitioning from stationary-to-driving by 15%. This was achieved by improving dataset label accuracy and increasing dataset size by 5%.

The upgrade in semantic detection means that the system now understands that a detected object is a school bus, rather than simply identifying it as a large vehicle or something else. This improvement is beneficial because it increases drivers' confidence around school buses, as they require a different driving behavior than most other vehicles on the road. In addition to improving semantic detection, a visualization of a school bus for better recognition would be very helpful. Finally, this recent upgrade to the system should allow it to detect vehicles transitioning from stationary to in motion more accurately, thereby making better decisions when navigating the road.

Improved detection of rare objects by 18% and reduced the depth error to large trucks by 9%, primarily from migrating to more densely supervised autolabeled datasets.

Object detection and depth perception advancements are essential for a safer FSD experience. Recent improvements in these areas include a 9% reduction in depth error for large trucks and an 18% increase in the ability to detect rare objects, thanks to densely supervised Auto label data sets. In addition, with better integration of multi-camera videos, the car can more accurately perceive the location and size of large trucks, reducing the risk of collisions and helping it stay in its lane. These enhancements increase the safety of everyone on the road and inspire greater confidence in autonomous driving technology.

Crosswalk Behavior will Change

Improved decision making at crosswalks by leveraging neural network based ego trajectory estimation in place of approximated kinematic models.

Engineers have found a new way to help Teslas make better decisions when dealing with crosswalks. In the past, FSD would try to stop as soon as possible when they saw a pedestrian near the crosswalk. However, this could be a problem because sometimes the pedestrian is just standing there and not planning to cross the street. To make things better, researchers have created a new computer model that helps the car make better decisions. This model is called "neural network-based ego trajectory estimation." With this model, the vehicle can decide whether to keep going or stop based on how close the pedestrian is to the crosswalk. This way, the car won't stop too early and won't cause any problems for other vehicles.

Highway Improvements

Added a long-range highway lanes network to enable earlier response to blocked lanes and high curvature.

Tesla's new long-range Highway Lanes Network will enable the car to respond earlier to blocked lanes and high curvature situations, typically on highways and high-speed roads. In addition, it addresses the limitations of the occupancy Network, which previously allowed the car to see only a limited distance of approximately 100 meters in front of and 20 meters behind the vehicle. With the new long-range Highway Lanes Network, the car can see further ahead, allowing it to detect blocked lanes and curves much sooner, giving it more time to react.

One of the most significant advantages of the long-range Highway Lanes Network is its ability to detect and respond to high curvature situations smoothly. Currently, the car brakes late before a curve, which is not optimal for a safe driving experience. However, with the long-range Highway Lanes Network, the car can predict the angle, making the acceleration smoother and braking earlier. This results in better behavior on highways and high-speed back roads. The new long-range Highway Lanes Network will also enhance the driving experience by reducing sudden braking, making the driving experience more comfortable for the passengers.

Improved reliability and smoothness of merge control, by deprecating legacy merge region tasks in favor of merge topologies derived from vector lanes.

The recent improvement to the highway merge control system involves merging topologies derived from Vector Lanes. Vector Lanes are dedicated lanes designed to make merging more efficient and less congested. They are situated to the left of the main highway lanes, providing extra space for merging vehicles to accelerate and merge smoothly into the main traffic. Vector Lanes are typically longer than traditional merging lanes, which gives drivers more time to complete their merge. The use of Vector Lanes, in combination with the modern merged topologies, can significantly improve the performance and safety of highway systems.

Improved lane changes, including: earlier detection and handling for simultaneous lane changes, better gap selection when approaching deadlines, better integration between speed-based and nav-based lane change decisions and more differentiation between the FSD driving profiles with respect to speed lane changes.

Improving full self-driving technology means enhancing lane change capabilities with earlier detection and handling for simultaneous changes, better gap selection, and enhanced speed and navigation-based data integration. A major challenge in full self-driving is the 10-30 second gap between navigation and second-to-second data, leaving room for critical lane change decisions. The improved system positions the car better, using the best time for lane changes by combining navigation and speed-based data.

Other Enhancements

Reduced goal pose prediction error for candidate trajectory neural network by 40% and reduced runtime by 3X. This was achieved by improving the dataset using heavier and more robust offline optimization, increasing the size of this improved dataset by 4X, and implementing a better architecture and feature space.

Now we get into some more technical changes in this update. Tesla has reduced "goal pose prediction error for candidate trajectory neural network by 40% and reduced runtime by 3X." First, let's take a step back. Goal pose refers to the position where the vehicle needs to end up. The candidate trajectory is the possible paths that the car could take to get there. The "goal pose prediction error" for the "candidate trajectory neural network" is the amount of difference between where the neural network predicts the vehicle will end up and where it ends up. In other words, it measures how accurately the neural network predicts the vehicle's final position. The goal is to minimize this prediction error so the car can accurately determine the best path to reach its goal pose. Therefore, these improvements equate to more precise estimates providing a better user experience.

Improved occupancy network detections by oversampling on 180K challenging videos including rain reflections, road debris, and high curvature.

Tesla also has improved its occupancy network detections specifically for rain reflections, road debris and high curvature. The occupancy network is a computer system that uses sensors to detect and identify objects in the environment, such as other vehicles or pedestrians, and determine whether or not they occupy space on the road. The "occupancy network detections" refer to detecting and identifying these objects in real-time as the vehicle is driving. For example, after rain, the program could pick up reflections on the road from nearby signs. In some cases, this could be very rare. Therefore engineers oversampled 180,000 videos to train the program on how to react.

Added "lane guidance module and perceptual loss to the Road Edges and Lines network, improving the absolute recall of lines by 6% and the absolute recall of road edges by 7%.

Improved overall geometry and stability of lane predictions by updating the "lane guidance" module representation with information relevant to predicting crossing and oncoming lanes.

The Lane guidance module and perceptual loss to the Road Edges and Lines network are two essential components of FSD technology. The Lane guidance module is responsible for identifying the lane markings on the road. In contrast, the Perpetual loss to the road edges and lines network analyzes images to detect the edges of the road and any obstacles in the car's path. These two components have been updated to improve the total recall of lines by six percent and road edges by seven percent, reducing false observations of road edges, lane edges, and other features where they should not be detected. The Lane guidance module may apply to all driving scenarios, including city and highway driving. Improvements to the module's representation can enhance the stability of Lane predictions, particularly in complex situations like intersections.

Unlocked longer fleet telemetry clips (by up to 26%) by balancing compressed IPC buffers and optimized write scheduling across twin SOCs.

Tesla made an impressive advancement in their Fleet Telemetry system by optimizing scheduling across twin SOCs and balancing compressed IPC buffers, leading to a 26% increase in telemetry data that can be sent back for analysis. IPC stands for inter-process communication, which is the communication between processes in a computer system, and SOC stands for system on a chip, a type of integrated circuit combining multiple computer components into one. In simpler terms, Tesla's improvement allows for better communication between two parallel chips in Hardware 3 and 4, which increases the amount of data they can send back for analysis. In addition, this extended telemetry timeframe from 10 to 12.5 or 13 seconds will enable Tesla to collect more contextual information, which can be useful for detecting objects earlier and avoiding potential road hazards.

Is Tesla Planning to Add Steam Support to All Vehicles?

Not a Tesla App

Yesterday, we reported that Tesla updated their Steam integration on Model S and Model X vehicles. The update was part of their 2024 Holiday Update, but it looks like there may be more to this than a simple update.

Steam, a video game library app, makes it easy for users to buy or launch games on their computers. However, a couple of years ago, Valve, who created Steam, launched their own standalone device, the Steam Deck. The Steam Deck runs a custom OS based on Linux.

Steam Launch

When Tesla launched the redesigned Model S and Model X, Tesla introduced a dedicated gaming GPU with 16GB of RAM and touted the ability to play top-tier PC games in Tesla vehicles.

In 2022, Tesla finally launched the Steam app for the Model S and Model X as part of its 2022 Holiday Update. The Steam app runs Steam OS, the same OS as the Steam Deck in a virtual environment.

However, earlier this year, Tesla stopped including the GPU and Steam (Beta) in their vehicles, and we haven’t seen any updates to the Steam in quite some time. In fact, we thought Tesla was axing their gaming-on-the-go dreams.

SteamOS Update

The Steam app, which is still in Beta, is getting an interesting update for the Model S and Model X vehicles with the discrete GPU.

Those vehicles received an update to SteamOS 3.6 - the same version of SteamOS that runs on the Steam Deck. While nothing has visually changed, there’s a long list of performance optimizations under the hood to get things running smoother.

Comparing Steam Deck to Tesla Vehicles

Let’s take a look at the Steam Deck - according to Valve, its onboard Zen4 CPU and GPU combined push a total of 2 TFlops of data, which is fairly respectable, but much lower than today’s home consoles. The Steam Deck is capable of 720p gaming fairly seamlessly on low-to-medium settings on the go and is also built on the AMD platform.

AMD-equipped Teslas, including the Model 3 and Model Y, are packing an older Zen+ (Zen 1.5) APU (processor with a combined CPU and GPU). AMD claims that the V1000 - the same embedded chip as on AMD Tesla vehicles (YE1807C3T4MFB), brings up to 3.6 TFLops of processing power with it, including 4K encoding and decoding with the integrated GPU on board.

While that’s not enough for 4K gaming or comparable to a full-blown console or desktop GPU, that’s enough raw horsepower for light gaming and is currently more powerful than the Steam Deck.

The Model S and Model X’s GPU brings that up to about 10TFlops of power - comparable to modern consoles like the Xbox Series X at 12 TFlops.

Steam Gaming for All Vehicles?

The fact that Tesla is updating SteamOS even though the feature is no longer available in any new vehicles could indicate that Tesla is not only bringing Steam back to Teslas but that it’s going to play a much bigger role.

While SteamOS is run in a virtual environment on top of Tesla’s own OS, we could see Tesla bring SteamOS to all of its current vehicles, including the Model 3, Model Y, and Cybertruck. Steam in these vehicles would likely support any game that’s capable of running on the Steam Deck.

We think this Steam update, which includes performance improvements and a variety of fixes, has quietly passed under most people’s radars. This could be a very exciting update for those who enjoy gaming, especially for those who love to do it in their Tesla.

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Tesla Holiday Update Weather Features: All the Small Details

Robert Rosenfeld / YouTube

As part of Tesla’s 2024 Holiday Update, Tesla included two awesome new features - Weather at Destination and the long-awaited Weather Radar Overlay. These two features are big upgrades built upon the weather feature that was added in update 2024.26. The original weather feature added an hourly forecast, as well as the chance of precipitation, UV index, Air Quality Index, and other data.

However, this update also added some smaller weather touches, such as the vehicle alerting you if the weather at the destination will be drastically different from the current weather.

Not a Tesla App

Weather At Destination

When you’re navigating to a destination and viewing the full navigation direction list, the text under the arrival time will show you the expected weather next to your destination. You can also tap this, and the full weather pop-up will show up, showing your destination's full set of weather information.

Note the weather under the arrival time

Not a Tesla App

You can also tap the weather icon at the top of the interface at any time and tap Destination to switch between the weather at your current location and the weather at your destination.

You’re probably considering that the weather at your destination doesn’t matter when you’re three hours away - but that’s all taken into account by the trip planner. It will add in both charge time and travel time and show you the weather at your destination at your expected arrival time.

And if the weather is drastically different or inclement, such as rain or snow, while you’ve got sunshine and rainbows - the weather will be shown above the destination ETA for a few moments before it tucks itself away.

Tesla also recently introduced a new voice command. Asking, “What’s the weather?” or something similar will now bring up Tesla’s weather popup.

The weather pop-up above the ETA

Not a Tesla App

One limitation, though—if you’re planning a long road trip that is more than a day of driving, the weather at destination feature won’t be available until you get closer.

Weather Radar Overlay

As part of the improvements to weather, Tesla has also added a radar overlay for precipitation. You can access the new radar overlay by tapping the map and then tapping the weather icon on the right side of the map. It’ll bring up a radar overlay centered on your vehicle. It’ll animate through the radar data over the last 3 hours so that you can see the direction of the storm, but you can also pause it at any point.

You’re able to scroll around in this view and see the weather anywhere, even if you zoom out. It also works while you’re driving, although it can be a little confusing if you’re trying to pay attention to the navigation system. If you like to have Points of Interest enabled on your map, the weather overlay will hide POIs except for Charging POIs.

Requirements / Data

Unfortunately, you’ll need Premium Connectivity for any of the weather features to work, and being on WiFi or using a hotspot will not be enough to get the data to show up. The data, including the weather radar, is provided by The Weather Channel.

As for supported models, weather and weather at destination are available on all vehicles except for the 2012-2020 Model S and Model X. The weather radar has more strict requirements and requires the newer AMD Ryzen-powered infotainment center available on the 2021+ Model S and Model X and more recent Model 3 and Model Y vehicles.

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