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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.Â
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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In a major development that will inevitably strengthen Tesla’s dominant position in the American EV market, Polestar has been effectively banned from selling new vehicles in the United States, starting with the 2027 model year.
The U.S. Department of Commerce denied Polestar authorization under the Connected Vehicle Rule, which prohibits vehicles containing certain connected technologies (Cellular, Wi-Fi, Bluetooth, etc.) linked to China or Russia due to national security risks, including potential data collection on American drivers.
🚨 A Tesla competitor goes down
Polestar will no longer sell new vehicles in the United States starting with the 2027 model year.
The U.S. Department of Commerce denied the brand authorization under the Connected Vehicle Rule, which restricts the sale of cars with software and… pic.twitter.com/TrwnQeoiES
— TESLARATI (@Teslarati) June 25, 2026
Polestar, which is majority-owned by China’s Geely Holding, could not obtain the required exemption despite producing some models domestically.
Polestar confirmed it will sell off any remaining inventory of the Polestar 3 and Polestar 4 models, while continuing service and warranty support for existing customers. No new models or major refreshes will reach U.S. buyers, and the company is pivoting its growth strategy to Europe, where it already generates the vast majority of its sales.
The outcome removes a direct premium EV competitor that had positioned itself as a stylish, performance-oriented alternative to Tesla’s lineup. The Polestar 2 challenged the Model 3, while the Polestar 3 and 4 targeted segments overlapping with the Model Y and upcoming Tesla offerings. Polestar’s U.S. sales had already been sluggish amid intense competition and slower demand, representing just 6 percent of its global volume in the first quarter of 2026.
While Polestar was not on Tesla’s level in the U.S., it still places a dent in the evergrowing field of Tesla competitors in the country, where it has long dominated EV sales.
Tesla faces none of these hurdles. As a U.S.-founded and U.S.-headquartered company with major manufacturing in Fremont, Austin, and Nevada, Tesla’s vehicles are built with compliant domestic and allied supply chains. Its Full Self-Driving technology, over-the-air software updates, and vertically integrated ecosystem were developed entirely in-house without foreign ownership entanglements that trigger national security reviews, at least in the U.S.
Of course, it did face a similar threat in China a few years back:
Elon Musk responds to reports of Tesla ban among China’s military over security concerns
The Connected Vehicle Rule, first advanced under the prior administration and upheld under the current one, is part of a broader U.S. effort to protect the domestic auto industry and critical technology from Chinese influence. High tariffs on Chinese-made EVs and related restrictions have already reshaped the market. Tesla benefits directly: it avoids these barriers while continuing to lead in U.S. EV sales volume, Supercharger network expansion, and energy storage integration.
By clearing Polestar from the new-vehicle playing field, the policy reduces competitive pressure in the premium and performance EV segments where Tesla has invested billions. American consumers seeking cutting-edge electric vehicles now have one fewer option tied to foreign adversaries — and one clearer path to the market leader that has driven the EV transition from the start.
For Tesla, this is more than regulatory relief. It is a strategic tailwind that reinforces its position as America’s premier EV innovator at a time when domestic manufacturing and technological independence matter most.
Tesla Cybercab stands to gain from new rules that the Trump Administration is aiming to enforce on autonomous vehicles. On Thursday, NHTSA, under the Trump Administration’s U.S. Department of Transportation, commenced rulemaking on the Federal Motor Vehicle Safety Standards (FMVSS).
This effort aims to eliminate the mandate for manual brake pedals in vehicles that are designed to be driven exclusively by automated driving systems. This would impact the Tesla Cybercab, which the company has stated would operate without a steering wheel or pedals.
Tesla Cybercab launch is imminent after latest sighting at Giga Texas
The Trump Administration is looking to revise FMVSS No. 135, which requires standard braking systems on light-duty vehicles.
Currently, the regulation requires light-duty cars to use traditional manual braking systems that allow operators to slow the vehicle. With the advent of self-driving in the U.S., these regulations need updating, and these are the changes that could come to FMVSS No. 135:
- Removes requirements for hand- or foot-operated brake controls for vehicles designed never to be operated by a human. Existing rules still apply to AVs that retain manual controls.
- All subject vehicles must still meet the same stopping distance performance criteria via alternative testing procedures.
- While this update ensures AVs can physically stop when commanded, NHTSA is separately developing safety performance requirements for AVs in real-world driving scenarios.
- NHTSA will continue to use its broad defect enforcement authority to investigate unsafe ADS behavior and oversee recalls.
As autonomy becomes a greater part of passenger travel, these types of rule adjustments will be more than reasonable. It will give manufacturers the ability to self-certify their vehicles and avoid any red tape that could ultimately delay the deployment of these vehicles.
Administrators are also incredibly excited about the opportunity to play a role in the advancement of self-driving vehicles.
“We are at the cusp of the greatest technological revolution in vehicle technology since the innovation of the Model T,” NHTSA Administrator Jonathan Morrison said. “If we want America to lead the way, we have to reimagine our regulatory framework. That’s why under Secretary Sean Duffy’s AV Framework, NHTSA is tearing down pointless barriers to innovative designs while strengthening the fundamental safety requirements that matter and holding AV developers accountable for safe performance.”
The Cybercab entered mass production at Gigafactory Texas in April. Tesla ultimately plans to push the vehicle into its Robotaxi fleet, potentially when frameworks like these are established.
Tesla plans to boost production at its Gigafactory Berlin plant in Germany following a sharp rebound in sales and demand in Europe after a softer 2025.
The plans put Tesla in a better position to compete with strengthening companies in Europe and potentially other markets; demand indicators show Tesla is much better off than in 2025.
Last year was a tough year for Tesla in terms of overall demand in Europe. The company produced over 200,000 vehicles at the German plant last year, a soft figure compared to the 375,000 vehicles Tesla lists as its current capacity at the factory.
🚨 Tesla said this morning it will ramp up production at Gigafactory Berlin to a volume of 7,500 vehicles per week.
This is a 20 percent boost in production. Tesla will hire 1,000 new employees to help with the increase.$TSLA pic.twitter.com/kravKfRO5n
— TESLARATI (@Teslarati) June 25, 2026
Tesla’s overall European sales dropped significantly last year due to a variety of factors. However, sales are rebounding, and demand is strong once again, and only getting stronger. Tesla is now planning to bump production of Model Y vehicles at Giga Berlin upward by about 20 percent. It will also bring 1,000 new jobs to the plant.
Tesla confirmed the details of its planned production expansion in Germany this morning. It is a strategy to keep up with strengthening demand.
In Q1, Tesla saw a record 61,000 vehicles produced at Giga Berlin. European registrations rebounded sharply, with Model Y seeing 117 percent increases in March 2026 compared to last year. Germany alone saw stark increases, with a quadrupling in registrations to 9,252 units.
This trend continued in other key European markets, including France, Denmark and Sweden. Tesla registrations were up over 46 percent in some of these markets, and Model Y continued its trend as a top BEV in the market.
Demand has been recovering strongly in 2026, giving Tesla a reason to expand production efforts at the factory. These increases signal management’s confidence in sustained or growing European pull for Berlin-built vehicles.
One of Tesla’s biggest threats just got banned in the U.S.
Tesla Cybercab stands to gain from new Trump autonomy rules
