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Tesla FSD Beta 10.69.2.2 extending to 160k owners in US and Canada: Elon Musk
It appears that after several iterations and adjustments, FSD Beta 10.69 is ready to roll out to the greater FSD Beta program. Elon Musk mentioned the update on Twitter, with the CEO stating that v10.69.2.2. should extend to 160,000 owners in the United States and Canada.
Similar to his other announcements about the FSD Beta program, Musk’s comments were posted on Twitter. “FSD Beta 10.69.2.1 looks good, extending to 160k owners in US & Canada,” Musk wrote before correcting himself and clarifying that he was talking about FSD Beta 10.69.2.2, not v10.69.2.1.
While Elon Musk has a known tendency to be extremely optimistic about FSD Beta-related statements, his comments about v10.69.2.2 do reflect observations from some of the program’s longtime members. Veteran FSD Beta tester @WholeMarsBlog, who does not shy away from criticizing the system if it does not work well, noted that his takeovers with v10.69.2.2 have been marginal. Fellow FSD Beta tester @GailAlfarATX reported similar observations.
Tesla definitely seems to be pushing to release FSD to its fleet. Recent comments from Tesla’s Senior Director of Investor Relations Martin Viecha during an invite-only Goldman Sachs tech conference have hinted that the electric vehicle maker is on track to release “supervised” FSD around the end of the year. That’s around the same time as Elon Musk’s estimate for FSD’s wide release.
It should be noted, of course, that even if Tesla manages to release “supervised” FSD to consumers by the end of the year, the version of the advanced driver-assist system would still require drivers to pay attention to the road and follow proper driving practices. With a feature-complete “supervised” FSD, however, Teslas would be able to navigate on their own regardless of whether they are in the highway or in inner-city streets. And that, ultimately, is a feature that will be extremely hard to beat.
Following are the release notes of FSD Beta v10.69.2.2, as retrieved by NotaTeslaApp:
– Added a new “deep lane guidance” module to the Vector Lanes neural network which fuses features extracted from the video streams with coarse map data, i.e. lane counts and lane connectivities. This architecture achieves a 44% lower error rate on lane topology compared to the previous model, enabling smoother control before lanes and their connectivities becomes visually apparent. This provides a way to make every Autopilot drive as good as someone driving their own commute, yet in a sufficiently general way that adapts for road changes.
– Improved overall driving smoothness, without sacrificing latency, through better modeling of system and actuation latency in trajectory planning. Trajectory planner now independently accounts for latency from steering commands to actual steering actuation, as well as acceleration and brake commands to actuation. This results in a trajectory that is a more accurate model of how the vehicle would drive. This allows better downstream controller tracking and smoothness while also allowing a more accurate response during harsh maneuvers.
– Improved unprotected left turns with more appropriate speed profile when approaching and exiting median crossover regions, in the presence of high speed cross traffic (“Chuck Cook style” unprotected left turns). This was done by allowing optimisable initial jerk, to mimic the harsh pedal press by a human, when required to go in front of high speed objects. Also improved lateral profile approaching such safety regions to allow for better pose that aligns well for exiting the region. Finally, improved interaction with objects that are entering or waiting inside the median crossover region with better modeling of their future intent.
– Added control for arbitrary low-speed moving volumes from Occupancy Network. This also enables finer control for more precise object shapes that cannot be easily represented by a cuboid primitive. This required predicting velocity at every 3D voxel. We may now control for slow-moving UFOs.
– Upgraded Occupancy Network to use video instead of images from single time step. This temporal context allows the network to be robust to temporary occlusions and enables prediction of occupancy flow. Also, improved ground truth with semantics-driven outlier rejection, hard example mining, and increasing the dataset size by 2.4x.
– Upgraded to a new two-stage architecture to produce object kinematics (e.g. velocity, acceleration, yaw rate) where network compute is allocated O(objects) instead of O(space). This improved velocity estimates for far away crossing vehicles by 20%, while using one tenth of the compute.
– Increased smoothness for protected right turns by improving the association of traffic lights with slip lanes vs yield signs with slip lanes. This reduces false slowdowns when there are no relevant objects present and also improves yielding position when they are present.
– Reduced false slowdowns near crosswalks. This was done with improved understanding of pedestrian and bicyclist intent based on their motion.
– Improved geometry error of ego-relevant lanes by 34% and crossing lanes by 21% with a full Vector Lanes neural network update. Information bottlenecks in the network architecture were eliminated by increasing the size of the per-camera feature extractors, video modules, internals of the autoregressive decoder, and by adding a hard attention mechanism which greatly improved the fine position of lanes.
– Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects.
– Improved recall of animals by 34% by doubling the size of the auto-labeled training set.
– Enabled creeping for visibility at any intersection where objects might cross ego’s path, regardless of presence of traffic controls.
– Improved accuracy of stopping position in critical scenarios with crossing objects, by allowing dynamic resolution in trajectory optimization to focus more on areas where finer control is essential.
– Increased recall of forking lanes by 36% by having topological tokens participate in the attention operations of the autoregressive decoder and by increasing the loss applied to fork tokens during training.
– Improved velocity error for pedestrians and bicyclists by 17%, especially when ego is making a turn, by improving the onboard trajectory estimation used as input to the neural network.
– Improved recall of object detection, eliminating 26% of missing detections for far away crossing vehicles by tuning the loss function used during training and improving label quality.
– Improved object future path prediction in scenarios with high yaw rate by incorporating yaw rate and lateral motion into the likelihood estimation. This helps with objects turning into or away from ego’s lane, especially in intersections or cut-in scenarios.
– Improved speed when entering highway by better handling of upcoming map speed changes, which increases the confidence of merging onto the highway.
– Reduced latency when starting from a stop by accounting for lead vehicle jerk.
– Enabled faster identification of red light runners by evaluating their current kinematic state against their expected braking profile.
Press the “Video Record” button on the top bar UI to share your feedback. When pressed, your vehicle’s external cameras will share a short VIN-associated Autopilot Snapshot with the Tesla engineering team to help make improvements to FSD. You will not be able to view the clip.
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Tesla is launching its solution to end Supercharger fights once and for all, eliminating any confusion on who is to charge next at a congested location.
Last year, a notable incident at a Tesla Supercharger led to a fight, and it all stemmed from a disagreement over who arrived at the location first.
Congestion at Tesla Superchargers is a pretty infrequent occurrence for most of us, but there are more congested and popular areas where wait times can be extensive. An unfortunate growing pain of EV ownership is the plain fact that chargers are not as available as gas pumps, and there are, at times, lines to charge.
This can cause tensions to flare and people to get entitled when visiting Superchargers. Nobody wants to spend hours at a Supercharger, but now, there will be no more confusion when there is a queue, and that’s thanks to Tesla’s new Virtual Queue for Superchargers.
Tesla is finally starting to build out the Virtual Supercharger Queue, according to Not a Tesla App, but it still relies on drivers to make it work.
When a driver is near a Supercharger that is full, a message will pop up on the Tesla App, using the driver’s location to determine their eligibility to join the virtual queue.
The app states:
“While the app is closed, Tesla uses your location to notify you of accurate wait times at Superchargers when you arrive.”
Another message within the app states:
“There is a waitlist to charge. Are you sure you want to start a charging session now?”
This sounds as if it will require drivers to act appropriately and only plug in when the app prompts them to do so, by letting them know it is their turn.
The app will notify the driver of their position in the queue, as well as how many vehicles are ahead of them.
Tesla launches first ‘true’ East Coast V4 Supercharger: here’s what that means
The company announced a while back that it would be working on a solution for this issue. Personally, I’ve only had to wait at a Supercharger for a charge on one occasion, and there was a line of between 3 and 10 cars during this singular occurrence.
I’m out at the Lancaster, PA Supercharger and showed up with a queue of three vehicles.
It’s now up to five and there have been several issues with order of arrival and confusion about who is first.
Any update on Supercharger queue? @elonmusk @aelluswamy @r_jegaa
— TESLARATI (@Teslarati) January 31, 2026
There were no conflicts or arguments about who had arrived first, but there was some discussion between several drivers during my time there about who was to charge first. Throw a non-Tesla EV into the mix, one that can only charge at a pull-in spot, and that causes even more of a complication.
News
Tesla offers awesome Free Supercharging incentive on an unexpected vehicle
In the past, Tesla has used Free Supercharging to incentivize the purchase of its expensive vehicles, like the Model S and Model X. However, those vehicles are leaving the company lineup, and Tesla saw a benefit from applying the incentive to another car.
Tesla is offering an awesome new Free Supercharging incentive on a vehicle that is sort of unexpected.
In the past, Tesla has used Free Supercharging to incentivize the purchase of its expensive vehicles, like the Model S and Model X. However, those vehicles are leaving the company lineup, and Tesla saw a benefit from applying the incentive to another car.
Tesla North America has introduced a compelling new incentive aimed at boosting Model 3 sales. Starting with orders placed on or after April 24, buyers of the Model 3 Premium (Long Range) and Performance variants in the United States will receive one full year of complimentary Supercharging.
The offer applies exclusively to new vehicle orders and does not extend to existing owners or other trims like the base Rear-Wheel Drive model.
New orders of Model 3 Premium & Performance now come with 1 year of free Supercharging 🇺🇸
Also, all Teslas pay the lowest Supercharging rates – all others pay a ~40% premium or need a subscription
— Tesla North America (@tesla_na) April 24, 2026
The announcement underscores Tesla’s continued dominance in EV charging infrastructure.
While the incentive provides 12 months of zero-cost access to the Supercharger network, Tesla also reiterated its pricing structure: all Tesla vehicles receive the lowest Supercharging rates.
Non-Tesla EVs, by contrast, pay approximately 40 percent more per kWh or must purchase a subscription to access the network at standard rates. This tiered approach highlights the strategic value of owning a Tesla, where seamless integration with the world’s largest and most reliable fast-charging network remains a key differentiator.
For prospective buyers, the savings can be substantial. Depending on driving habits, a typical Model 3 owner might log 12,000–15,000 miles annually.
With average Supercharging costs around $0.40–$0.50 per kWh, one year of free sessions could translate to $800–$1,200 in avoided expenses.
That effectively lowers the total cost of ownership and makes long-distance travel more affordable from day one. Early delivery customers have already noted similar past incentives, with one Cybertruck owner reporting over $2,400 saved in just six months under similar offers that Tesla has deployed in the past.
The timing of the offer appears strategic. Tesla faces growing competition from other automakers expanding their own charging networks and offering aggressive EV incentives.
By bundling free Supercharging rather than discounting the vehicle’s MSRP, Tesla preserves perceived value while directly addressing one of the biggest barriers for new EV adopters: charging costs and convenience.
The move also encourages higher-mileage use of the network, generating valuable real-world data for Tesla’s autonomous driving development.
Why Tesla would apply this incentive to the Model 3 is pretty interesting. It usually is a pretty good incentive to move units out the door, so there’s some speculation whether Tesla is planning to launch new upgrades to the mass-market sedan in the coming months, and the company wants to move what will be outdated units from its inventory.
However, there is also just the idea that Tesla could be attempting to stimulate some early quarter demand for the Model 3, especially as the Model Y continues to sell very well. Tesla’s loss of the $7,500 EV tax credit last year had an impact on sales, and Tesla might be testing some formidable options to see if it can add some demand once again.
News
Tesla Cybercab gets crazy change as mass production begins
Tesla has officially kicked off mass production of its groundbreaking Cybercab robotaxi at Giga Texas, and the first units rolling off the line feature a striking transformation that’s turning heads across the EV community.
Tesla Cybercab has evidently received a pretty crazy change from an aesthetic standpoint, as the company has made the decision to offer an additional finish on the vehicle as mass production is starting.
Tesla has officially kicked off mass production of its groundbreaking Cybercab robotaxi at Giga Texas, and the first units rolling off the line feature a striking transformation that’s turning heads across the EV community.
VIN Zero—the very first production Cybercab—showcases a vibrant champagne gold exterior with a high-gloss finish, a dramatic departure from the flat, matte-wrapped prototypes that debuted at the 2024 “We, Robot” event.
Presenting VIN Zero — the very first production Cybercab built at Giga Texas. pic.twitter.com/8bXo4CJAlr
— TechOperator (@TechOperator) April 23, 2026
This glossy sheen is a pretty big pivot from what was initially shown by Tesla. The company has maintained a pretty flat tone in terms of anything related to custom colors or finishes.
A specialized clear coat or process delivers the deep, reflective gloss without conventional painting. The result is a premium, mirror-like shine, and it looks pretty good, and gives the compact two-seater a more luxurious and futuristic presence than the subdued matte prototypes.
Photos shared by Tesla community members reveal VIN Zero in a showroom-like setting at Giga Texas, highlighting refined panel gaps, large aero wheel covers, and the signature no-steering-wheel, no-pedals interior optimized for full autonomy.
The open frunk in some images offers a glimpse of practical storage, while the overall build quality appears more polished than that of test mules.
This glossy evolution aligns with Tesla’s broader production ramp. After the first unit in February 2026, the company has shifted to volume manufacturing, with dozens of units already spotted in outbound lots. CEO Elon Musk and the team aim for hundreds per week, paving the way for unsupervised FSD robotaxi networks that could slash ride costs to pennies per mile.
The Cybercab holds Tesla’s grand ambitions of operating a full-service ride-hailing service without any drivers in its grasp. Tesla has yet to solve autonomy, but is well on its way, and although its timelines are usually a bit off, improvements often come through the Over-the-Air updates to the Full Self-Driving suite.
The FCC just said ‘No’ to SpaceX for now
Tesla launches solution to end Supercharger fights once and for all
