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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Tesla appears to be preparing to expand its Robotaxi geofence in Austin, Texas, with numerous engineering vehicles taking to the road. One of the most interesting sights, between the short and tall LiDAR rigs, was a Cybertruck validation vehicle, which we don’t often see.
Tesla’s expansion is moving the Robotaxi Network into downtown Austin, a dense urban environment that is currently outside the geofence. It appears Tesla is content with the latest builds of Robotaxi FSD and is ready to take on urban traffic.
The inclusion of a Cybertruck in the validation fleet is noteworthy, as the rest of the vehicles are Model Ys. This suggests that Tesla may be addressing two challenges simultaneously: expanding its service area while also addressing the FSD gap between the Cybertruck and other HW4 Tesla vehicles.
Tesla Validating Downtown Austin before expanding the Robotaxi geo-fence area. pic.twitter.com/ylFATtjcDi
Recent sightings have shown a fleet of Tesla vehicles, equipped with rooftop validation sensor rigs, running routes throughout downtown Austin and across the South Congress Bridge. While these rigs include LiDAR, it’s not a sign that Tesla is abandoning its vision-only approach.
Instead, Tesla uses the high-fidelity data from the LiDAR as a ground truth measurement to validate and improve the performance of its cameras. In short, it essentially uses the LiDAR measurements as the actual distances and then compares the distances determined in vision-only to the LiDAR measurements. This allows Tesla to tweak and improve its vision system without needing LiDAR.
This data collection in a new, complex environment right outside the Robotaxi geofence is an indicator that plans to expand the geofence. Tesla has previously indicated that they intend to roll out more vehicles and expand the geofence slowly. Given that their operational envelope includes the entire Austin Metro Area, we can expect more locations to open up gradually.
Once they expand the operational radius to include downtown Austin, they will likely also have to considerably increase the number of Robotaxis active in the fleet at any given time. Early-access riders are already saying that the wait time for a Robotaxi is too long, with them sometimes having to wait 15 minutes to be picked up.
With a larger service area, we expect Tesla to also increase the number of vehicles and the number of invited riders to try out the service.
After all, Tesla’s goal is to expand the Robotaxi Network to multiple cities within the United States by the end of 2025. Tesla has already been running an employees-only program in California, and we’ve seen validation vehicles as far away as Boston and New Jersey, on the other side of the country.
Cyber FSD Lagging Behind
One of the most significant details from these recent sightings is the presence of a Cybertruck. Cybertruck’s FSD builds have famously lagged behind the builds available on the rest of Tesla’s HW4 fleet. Key features that were expected never fully materialized for the Cybertruck, and the list of missing features is quite extensive.
Start FSD from Park
Improved Controller
Reverse on FSD
Actually Smart Summon
It may not look like a lot, but if you drive a Cybertruck on FSD and then hop in any of the rest of Tesla’s HW4 vehicles, you’ll notice a distinct difference. This is especially evident on highways, where the Cybertruck tends to drift out of the lane, often crossing over the lane markings.
Tesla was testing parts of Downtown Austin, TX with this Cybertruck which had a massive roof rack, and sensors.
We previously released an exclusive mentioning that a well-positioned internal source confirmed with us that a new FSD build for the Cybertruck was upcoming, but we never ended up receiving that particular build, only a point release to V13.2.9. The AI team’s focus had clearly shifted to getting the latest Robotaxi builds running and validated, and while a flagship, the Cybertruck fleet was small and new, and really a secondary task.
The Cybertruck’s larger size, steer-by-wire, rear-wheel steering, and different camera placements likely present a bigger set of challenges for FSD. Deploying it now as a validation vehicle in a complex environment like downtown Austin suggests that Tesla is finally gathering the specific data needed to bring the Cybertruck’s capabilities up to par. This focused effort is likely the necessary step to refine FSD’s handling of the Cybertruck before they begin rolling out new public builds.
When?
Once Tesla’s validation is complete, we can probably expect the Robotaxi Network to expand its borders for the first time in the coming days or weeks. However, we’ll likely see more signs of the expansion, such as Robotaxi vehicles driving themselves around the area, before the expansion actually happens.
Hopefully, the Cybertruck will also learn from its older siblings and receive the rest of its much-needed FSD features, alongside an FSD update for the entire fleet.
Tesla is rolling out a fairly big update for its iOS and early-access-only Robotaxi app, delivering a suite of improvements that address user feedback from the initial launch last month. The update improves the user experience with increased flexibility, more information, and overall design polish.
The most prominent feature in this update is that Tesla now allows you to adjust your pickup location. Once a Robotaxi arrives at your pickup location, you have 15 minutes to start the ride. The app will now display the remaining time your Robotaxi will wait for you, counting down from 15:00. The wait time is also shown in the iOS Live Activity if your phone is on the lock screen.
How Adjustable Pickups Work
We previously speculated that Tesla had predetermined pickup locations, as the pickup location wasn’t always where the user was. Now, with the ability to adjust the pickup location, we can clearly see that Tesla has specific locations where users can be picked up.
Rather than allowing users to drop a pin anywhere on the map, the new feature works by having the user drag the map to their desired area. The app then presents a list of nearby, predetermined locations to choose from. Once a user selects a spot from this curated list, they hit “Confirm.” The pickup site can also be changed while the vehicle is en route.
This specific implementation raises an interesting question: Why limit users to predetermined spots? The answer likely lies in how Tesla utilizes fleet data to improve its service.
Here is the new Tesla Robotaxi pickup location adjustment feature.
While the app is still only available on iOS through Apple’s TestFlight program, invited users can download and update the app.
Tesla included these release notes in update 25.7.0 of the Robotaxi app:
You can now adjust pickup location
Display the remaining wait time at pickup in the app and Live Activity
Design improvements
Bug fixes and stability improvements
Nic Cruz Patane
Why Predetermined Pick Up Spots?
The use of predetermined pickup points is less of a limitation and more of a feature. These curated locations are almost certainly spots that Tesla’s fleet data has identified as optimal and safe for an autonomous vehicle to perform a pickup or drop-off.
This suggests that Tesla is methodically “mapping” its service area not just for calibration and validation of FSD builds but also to help perform the first and last 50-foot interactions that are critical to a safe and smooth ride-hailing experience.
An optimal pickup point likely has several key characteristics identified by the fleet, including:
A safe and clear pull-away area away from traffic
Good visibility for cameras, free of obstructions
Easy entry and exit paths for an autonomous vehicle
This change to pick-up locations reveals how Tesla’s Robotaxi Network is more than just Unsupervised FSD. There are a lot of moving parts, many of which Tesla recently implemented, and others that likely still need to be implemented, such as automated charging.
Frequent Updates
This latest update delivers a much-needed feature for adjusting pickup locations, but it also gives us a view into exactly what Tesla is doing with all the data it is collecting with its validation vehicles rolling around Austin, alongside its Robotaxi fleet.
Tesla is quickly iterating on its app and presumably the vehicle’s software to build a reliable and predictable network, using data to perfect every aspect of the experience, from the moment you hail the ride to the moment you step out of the car.
![Tesla Updates Robotaxi App: Adds Adjustable Pick Up Locations, Shows Wait Time and More [VIDEO]](https://www.notateslaapp.com/img/containers/article_images/tesla-app/robotaxi-app/25-7-0/robotaxi-app-25.7.0.webp/4ac9ed40be870cfcf6e851fce21c43b9/robotaxi-app-25.7.0.jpg)
