Tesla's latest FSD Beta, v10.11 is now going out to public testers. The beta is version 2022.4.5.15. For FSD Beta testers, it'll be the first update they receive that's based on a 2022 release.
Earlier this month Elon tweeted that the beta may go out as early as this past Tuesday. However, he then followed up that it was instead going to go out this past weekend.
Over the weekend we saw FSD Beta 10.11 go out to several employees, which Tesla uses as a final testing phase before releasing to the public.
Today we're finally seeing several public testers getting this build, but it may be a while before it goes out to everyone. Tesla looks at the release carefully as it's going out and can choose to slow it down, speed it up or stop it completely to fix any issues.
When Elon spoke about the next FSD Beta, he mentioned FSD Beta 10.12. This beta is version 10.11. It's not clear whether there was a misunderstanding or whether Tesla initially planned to increment the version.
However, this is a completely new beta for all public testers and it appears to be packed with improvements.
The most notable improvements appear to be new vector-based lanes and reduced slowdowns. An example of the new vector-based lanes is below. In addition to clearer lane markings, it appears that the whole lane will also be highlighted in blue when the car starts to perform a lane change.
New vector lanes
@MarkHalleyPhd/Twitter
This beta is expected to hit Canada for the first time according to Elon, but there are no signs yet of it going north of the border.
Tesla will likely monitor it for several days in the US before releasing it to our northern neighbor.
The complete FSD Beta release notes are below:
- Upgraded modeling of lane geometry from dense rasters ("bag of points") to an autoregressive decoder that directly predicts and connects "vector space" lanes point by point using a transformer neural network. This enables us to predict crossing lanes, allows computationally cheaper and less error prone post-processing, and paves the way for predicting many other signals and their relationships jointly and end-to-end.
- Use more accurate predictions of where vehicles are turning or merging to reduce unnecessary slowdowns for vehicles that will not cross our path.
- Improved right-of-way understanding if the map is inaccurate or the car cannot follow the navigation. In particular, modeling intersection extents is now entirely based on network predictions and no longer uses map-based heuristics.
- Improved the precision of VRU detections by 44.9%, dramatically reducing spurious false positive pedestrians and bicycles (especially around tar seams, skid marks, and rain drops). This was accomplished by increasing the data size of the next-gen autolabeler, training network parameters that were previously frozen, and modifying the network loss functions. We find that this decreases the incidence of VRU-related false slowdowns.
- Reduced the predicted velocity error of very close-by motorcycles, scooters, wheelchairs, and pedestrians by 63.6%. To do this, we introduced a new dataset of simulated adversarial high speed VRU interactions. This update improves autopilot control around fast-moving and cutting-in VRUs.
- Improved creeping profile with higher jerk when creeping starts and ends.
- Improved control for nearby obstacles by predicting continuous distance to static geometry with the general static obstacle network.
- Reduced vehicle "parked" attribute error rate by 17%, achieved by increasing the dataset size by 14%. Also improved brake light accuracy.
- Improved clear-to-go scenario velocity error by 5% and highway scenario velocity error by 10%, achieved by tuning loss function targeted at improving performance in difficult scenarios.
- Improved detection and control for open car doors.
- Improved smoothness through turns by using an optimization-based approach to decide which road lines are irrelevant for control given lateral and longitudinal acceleration and jerk limits as well as vehicle kinematics.
- Improved stability of the FSD Ul visualizations by optimizing ethernet data transfer pipeline by 15%.
- Improved recall for vehicles directly behind ego, and improved precision for vehicle detection network.
Release Notes Explained
Here is a great video that explains Tesla's technical release notes and what improvements you can find in this release.
In addition to the improvements in this FSD Beta, testers can also expect to find these other features that were added in the 2022.4 update.
Range Display Calibration for LFP batteries
If you have a SR+ with an LFP battery, then you'll also receive this feature that charges your car to 100% to help improve battery calibration. LFP batteries have very similar voltages from a low state of charge to a high state of charge. If the battery isn't regularly charged to 100%, it can be difficult for the vehicle to know its state of charge, which could cause some issues.
Cabin Camera
Tesla is collecting additional analytics from the cabin camera to help develop additional features. Tesla is asking you to opt-in to cabin camera analytics if you'd like to help develop new features.
There's no word on what these new features may be, but it could be just about anything, such as the ability to send you a notification if it detects an animal in your car and you forgot to turn on Dog Mode.
Car Colorizer
We're probably all familiar with this feature by now that allows you to alter the exterior color of your vehicle. The color you pick is used in the car's visualizations, car menus and in the Tesla app. You can also view a video of Tesla's Car Colorizer feature.
Audio Sources
The ability to disable certain audio sources comes back in 2022.4. If there are audio sources that you don't use, such as TIDAL, Spotify, or TuneIn, you can now disable them.
When an audio source is disabled, it won't appear in the More Apps menu or in the Sources dropdown.
Icons in the Status Bar
2022.4 was released quite a while ago, so it's easy for FSD Beta testers to forget everything that is in this release and why they should be excited.
Some icons are now returning to the car's top status bar, such as Driver Profiles (while in park) and the Sentry Mode icon.
Save Dashcam Clips
You can now more easily save dashcam clips if you have the Dashcam viewer in your launcher. Since the dashcam viewer can't be used while driving, the icon now has a dual purpose. If you tap it while in Drive, your car will save the last ten minutes of footage.
Regenerative Braking in Autopilot
Additional regenerative braking is now used in Autopilot, which will be especially useful in FSD. The vehicle previously used regenerative braking while on AP, but it will now apply it at lower speeds that better match how a driver would use regenerative braking.
Windshield Wiper Defrost
If you have a new Tesla that was built in the past few months, then it may have windshield wiper heaters. If it does, then this is the software update that enables it.
Nearby Superchargers
You can once again view nearby Superchargers in the same way you could in Tesla's v10 software. The Superchargers icon now appears on the far side just like it used to.
This FSD Beta release is an exciting one that includes many new features with the updated FSD Beta build and in the public 2022.4 release. You can also view the full 2022.4 release notes.
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The Super Manifold is Tesla’s solution to reducing the complexity of a heat pump system for an EV. Tesla showed off its engineering chops back with the original Model Y in 2019, where it introduced a new 8-way valve (the Octovalve) and a new heat pump alongside the uniquely designed Super Manifold to improve efficiency.
Now, Tesla is launching an improved version with the refreshed Model Y - the Super Manifold V2. We got to hear about it thanks to Sandy Munro’s interview with Tesla’s Lars Moravy (Vice President of Vehicle Engineering) and Franz Von Holzhausen (Chief of Vehicle Design). You can watch the video further below.
What Is The Super Manifold?
The Super Manifold (get it, Superman?), is an all-in-one package that brings in all the components of a heat pump system into one component. The Super Manifold packs all the refrigerant and coolant components around a 2-layer PCB (printed circuit board).
This Super Manifold would normally have 15 or 20 separate components, but Tesla managed to integrate them all into one nice package. That presented Tesla with a new challenge: how to integrate a heat pump—capable of both heating and cooling—into a single, efficient platform?
Several years ago, Tesla designed the Octovalve. It combines inlets and outlets and can variably change between heating or cooling on the fly - without needing to be plumbed in different directions. This is especially important for EVs, which may need to heat the battery with the waste heat generated from the motors or the heat pump while also cooling the cabin - or vice versa.
Original Super Manifold V1.1
Tesla launched the Super Manifold V1.1 back in 2022, and it provided some minor improvements to the waste heat processing of the heat exchange system. It also tightened up the Octovalve, preventing the leakage of oils into the HVAC loop that could cause it to freeze at extremely low temperatures.
Tesla has been using the V1.1 for several years now, and it has really solved the vast majority of issues with the heat pump system that many older Model Ys experienced.
Super Manifold V2 Coming Soon
Now, Tesla is introducing the Super Manifold V2 in the new Model Y. It will improve the overall cooling capacity provided by the original Super Manifold, but unfortunately, not every single new Model Y will come with it equipped. Tesla will be introducing it slowly across the lineup and at different rates at different factories, depending on part availability.
Eventually, the Super Manifold V2 will also make its way to other vehicles, potentially including the upcoming refresh for the Model S and Model X, but initially, it’ll be exclusive to the new Model Y. Tesla expects to have the new manifold in every new Model Y later this year.
If you’re interested in checking out the whole video, we’ve got it for you below.
Mark Rober, of glitter bomb package fame, recently released a video titled Can You Fool A Self-Driving Car? (posted below). Of course, the vehicle featured in the video was none other than a Tesla - but there’s a lot wrong with this video that we’d like to discuss.
We did some digging and let the last couple of days play out before making our case. Mark Rober’s Wile E. Coyote video is fatally flawed.
The Premise
Mark Rober wanted to prove whether or not it was possible to fool a self-driving vehicle, using various test scenarios. These included a wall painted to look like a road, low-lying fog, mannequins, hurricane-force rain, and bright beams.
All of these individual “tests” had their own issues - not least because Mark didn’t adhere to any sort of testing methodology, but because he was looking for a result - and edited his tests until he was sure of it.
Interestingly, many folks on X were quick to spot that Mark had been previously sponsored by Google to use a Pixel phone - but was using an iPhone to record within the vehicle - which he had edited to look like a Pixel phone for some reason. This, alongside other poor edits and cuts, led many, including us, to believe that Mark’s testing was edited and flawed.
Flaw 1: Autopilot, Not FSD
Let’s take a look at the first flaw. Mark tested Autopilot - not FSD. Autopilot is a driving aid for lane centering and speed control - and is not the least bit autonomous. It cannot take evasive maneuvers outside the lane it is in, but it can use the full stable of Tesla’s extensive features, including Automatic Emergency Braking, Forward Collision Warnings, Blind Spot Collision Warnings, and Lane Departure Avoidance.
On the other hand, FSD is allowed and capable of departing the lane to avoid a collision. That means that even if Autopilot tried to stop and was unable to, it would still impact whatever obstacle was in front of it - unlike FSD.
As we continue with the FSD argument - remember that Autopilot is running on a 5-year-old software stack that hasn’t seen updates. Sadly, this is the reality of Tesla not updating the Autopilot stack for quite some time. It seems likely that they’ll eventually bring a trimmed-down version of FSD to replace Autopilot, but that hasn’t happened yet.
Mark later admitted that he used Autopilot rather than FSD because “You cannot engage FSD without putting in a destination,” which is also incorrect. It is possible to engage FSD without a destination, but FSD chooses its own route. Where it goes isn’t within your control until you select a destination, but it tends to navigate through roads in a generally forward direction.
The whole situation, from not having FSD on the vehicle to not knowing you can activate FSD without a destination, suggests Mark is rather unfamiliar with FSD and likely has limited exposure to the feature.
Let’s keep in mind that FSD costs $99 for a single month, so there’s no excuse for him not using it in this video.
Flaw 2: Cancelling AP and Pushing Pedals
Many people on X also followed up with reports that Mark was pushing the pedals or pulling on the steering wheel. When you tap on the brake pedal or pull or jerk the steering wheel too much, Autopilot will disengage. For some reason, during each of his “tests,” Mark closely held the steering wheel of the vehicle.
This comes off as rather odd - at the extremely short distances he was enabling AP at, there wouldn’t be enough time for a wheel nag or takeover warning required. In addition, we can visibly see him pulling the steering wheel before “impact” in multiple tests.
Over on X, techAU breaks it down excellently on a per-test basis. Mark did not engage AP in several tests, and he potentially used the accelerator pedal during the first test - which means that Automatic Emergency Braking is overridden. In another test, Mark admitted to using the pedals.
Flaw 3: Luminar Sponsored
This video was potentially sponsored by a LiDAR manufacturer - Luminar. Although Mark says that this isn’t the case. Interestingly, Luminar makes LiDAR rigs for Tesla - who uses them to test ground truth accuracy for FSD. Just as interesting, Luminar’s Earnings Call was also coming up at the time of the video’s posting.
Luminar had linked the video at the top of their homepage but has since taken it down. While Mark did not admit to being sponsored by Luminar, there appear to be more distinct conflicts of interest, as Mark’s charity foundation has received donations from Luminar’s CEO.
Given the positivity of the results for Luminar, it seems that the video had been well-designed and well-timed to take advantage of the current wave of negativity against Tesla, while also driving up Luminar’s stock.
Flaw 4: Vision-based Depth Estimation
The next flaw to address is the fact that humans and machines can judge depth using vision. On X, user Abdou ran the “invisible wall” through a monocular depth estimation model (DepthAnythingV2) - one that uses a single image with a single angle. This fairly simplified model can estimate the distance and depth of items inside an image - and it was able to differentiate the fake wall from its surroundings easily.
Tesla’s FSD uses a far more advanced multi-angle, multi-image tool that stitches together and creates a 3D model of the environment around it and then analyzes the result for decision-making and prediction. Tesla’s more refined and complex model would be far more able to easily detect such an obstacle - and these innovations are far more recent than the 5-year-old Autopilot stack.
While detecting distances is more difficult in a single image, once you have multiple images, such as in a video feed, you can more easily decipher between objects and determine distances by tracking the size of each pixel as the object approaches. Essentially, if all pixels are growing at a constant rate, then that means it’s a flat object — like a wall.
Case in Point: Chinese FSD Testers
To make the case stronger - some Chinese FSD testers took to the streets and put up a semi-transparent sheet - which the vehicle refused to drive through or drive near. It would immediately attempt to maneuver away each time the test was engaged - and refused to advance with a pedestrian standing in the road.
Would FSD hit a transparent film wall? This test showed it just avoids it.
Thanks to Douyin and Aaron Li for putting this together, as it makes an excellent basic example of how FSD would handle such a situation in real life.
Flaw 5: The Follow-Up Video and Interview
Following the community backlash, Mark released a video on X, hoping to resolve the community’s concerns. However, this also backfired. It turned out Mark’s second video was of an entirely different take than the one in the original video - this was at a different speed, angle, and time of initiation.
Mark then followed up with an interview with Philip DeFranco (below), where he said that there were multiple takes and that he used Autopilot because he didn’t know that FSD could be engaged without a destination. He also answered here that Luminar supposedly did not pay him for the video - even with their big showing as the “leader in LiDAR technology” throughout the video.
Putting It All Together
Overall, Mark’s video was rather duplicitous - he recorded multiple takes to get what he needed, prevented Tesla’s software from functioning properly by intervening, and used an outdated feature set that isn’t FSD - like his video is titled.
Upcoming Videos
Several other video creators are already working to replicate what Mark “tried” to test in this video.
To get a complete picture, we need to see unedited takes, even if they’re included at the end of the video. The full vehicle specifications should also be disclosed. Additionally, the test should be conducted using Tesla’s latest hardware and software—specifically, an HW4 vehicle running FSD v13.2.8.
In Mark’s video, Autopilot was engaged just seconds before impact. However, for a proper evaluation, FSD should be activated much earlier, allowing it time to react and, if capable, stop before hitting the wall.
A wave of new videos is likely on the way—stay tuned, and we’ll be sure to cover the best ones.
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