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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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In the world of autonomous ride-hailing, there are only a handful of names. Among those few companies lies a strategy play by each to keep the opposition on their toes. Tesla, on the other hand, already tipped its hand at where it is headed next.
Tesla has signaled its next major push in the autonomous ride-hailing market by filing for an Autonomous Vehicle Network Company permit in Nevada (Docket 26-05015). Through Tesla Robotaxi, LLC, the company seeks approval to operate up to 5,000 robotaxis in Clark County, including high-traffic areas like Las Vegas and Henderson airports, within the first 12 months of launch.
This filing builds on Tesla’s earlier testing approvals from the Nevada DMV in September 2025 and preparations such as maintenance hubs in the Las Vegas area. Nevada represents a strategic expansion into a major tourist destination, where high visitor volumes could drive strong utilization and showcase the reliability of unsupervised autonomy to a broad audience.
We’d have to assume this means Tesla is targeting Las Vegas, and it’s a great move from a business perspective.
Vegas is such a melting pot of people from all around the country and the world. It will expose people from all corners of the globe to Tesla’s autonomy capabilities https://t.co/Qz3fQmhULF pic.twitter.com/Du5pj2RyWC
— TESLARATI (@Teslarati) June 6, 2026
Approval would mark a significant step toward commercial operations in a new state, following progress in Texas.
Tesla’s shareholder decks and earnings calls have clearly outlined these ambitions. In the Q4 2025 shareholder deck, the company listed planned Robotaxi coverage for the first half of 2026, explicitly naming Las Vegas alongside Phoenix, Miami, Orlando, and Tampa, with Dallas and Houston already advancing. Austin was noted as “ramping unsupervised,” while the Bay Area remained in safety-driver mode.
By Q1 2026, the deck updated statuses to reflect launches in Dallas and Houston, with “preparations underway” for the remaining cities, including Las Vegas. Paid Robotaxi miles nearly doubled sequentially in Q1, underscoring momentum even as broader timelines adjusted slightly for regulatory and operational readiness.
On earnings calls, CEO Elon Musk and executives have emphasized a phased rollout prioritizing safety. Unsupervised operations in Texas have shown strong results with no reported accidents or injuries in the program. Tesla continues groundwork in additional major U.S. metros through testing and permitting, positioning it to scale quickly once approvals clear.
This Nevada move aligns with Tesla’s vision of transforming from an EV maker into an AI and robotics leader. The forthcoming Cybercab, which started production at Giga Texas in April, is expected to eventually dominate the fleet, replacing many Model Y vehicles and driving down costs to enable affordable rides.
For investors and the industry, this signals Tesla’s intent to dominate key Sun Belt and tourist markets where weather, regulations, and demand favor rapid scaling. Success in Las Vegas could validate the model for denser urban and high-tourism environments, accelerating the shift toward a future where robotaxis generate meaningful revenue.
Las Vegas will also expand knowledge among the general public at Tesla’s capabilities, helping people experience driverless ride-hailing from several companies during their time on The Strip.
Investor's Corner
Tesla just did something in South Korea that no foreign carmaker has ever done
Tesla’s Model Y just became South Korea’s best-selling car, beating every domestic model in May.
Tesla did something last month that no foreign car has ever done in South Korea by outselling every vehicle in the country, domestic or imported, finishing the month with Model Y as the single best-selling car across the entire Korean market. According to data from the Korea Automobile Importers and Distributors Association released on June 4, the Model Y recorded 8,762 units sold in May, pushing the Kia Sorento into second place at 7,836 units and the Hyundai Grandeur into third at 5,183 units. It is the first time an imported vehicle has outsold every domestic model on a single-month basis.
Tesla imported 10,866 cars into South Korea in May, making it the top import brand for the fourth consecutive month. BMW followed at 6,555 units, less than two-thirds of Tesla’s total, while BYD registered just 1,032 units. The combined domestic sales of GM Korea, Renault Korea, and KG Mobility last month totaled just 7,019 units, meaning a single Tesla model outsold three Korean automakers combined.
Tesla FSD earns high praise in South Korea’s real-world autonomous driving test
South Korea has historically been one of the hardest markets for foreign automakers to crack. Hyundai and Kia together control close to 70% of the overall market and carry deep consumer loyalty built over decades. Tesla’s path into this market was an uphill battle due to high import duties, limited service infrastructure, and early skepticism about charging networks. In 2024, the Model Y was the best-selling imported car in South Korea with 18,717 units for the full year. By 2025, after the Juniper refresh, it cleared 50,000 units and took the top spot among all EVs.
Year to date, Tesla has a 250.8% increase in the country over the same period last year, and now holds a 30.8% share of the entire imported car segment for 2026. EVs as a category represented 48.6% of all imported passenger car registrations in May. As Teslarati has reported, the Juniper refresh brought meaningful improvements to range, interior quality, and ride refinement that addressed the most common criticisms of earlier Model Y versions. Those upgrades appear to be resonating in markets like South Korea where buyers compare Tesla directly against high end domestic competitors.
The Tesla Model 3’s cheapest trim level just got a major accolade, as Edmunds just revealed the Rear-Wheel-Drive trim of the all-electric sedan is the most efficient EV that is currently in production.
The 2026 Tesla Model 3 Rear-Wheel-Drive not only beat its EPA-estimated range by 30 miles, but it also bested its efficiency mark by 13.2 percent. The Model 3 tested by Edmunds traveled 393 miles, beating its EPA rating by 8.3 percent, while it returned 21.7 kWh per 100 miles, or 4.61 mi/kWh.
Beating those two metrics is especially pertinent when it comes to EV ownership and driving down the cost of ownership from ICE counterparts across the board. The real money savings come from driving down the cost of driving per mile, especially when it comes to high-mileage driving.
Edmunds stated in its report and review that the process it uses to test EV efficiency is aimed at giving “the most accurate representation of a car’s real-world range.” The assessment uses a strict route that features 60 percent city and 40 percent highway driving, and an average speed of 40 MPH across the trip.
It also drives each car within 5 MPH of all posted speed limits, and the climate control is set on Auto at 72 degrees to ensure even testing. In other words, Edmunds does not use methods to maximize efficiency, and instead tries to make it reasonable to achieve the same ratings yourself.
In comparison to other EVs, it beat the 2026 Mercedes-Benz CLA 350, which went 385 miles, as well as the 2026 Audi A6 Sportback E-tron Prestige AWD, which traveled 392 miles. Only the Mercedes-Benz CLA 250+ traveled farther, making it an impressive 434 miles on a charge.
However, the Tesla Model 3 RWD’s efficiency is “unmatched” because of its incredibly low energy usage per mile.
🚨 Tesla Model 3 RWD:
-At $36,990, it is $9,000 cheaper than the average transaction price for a new car ($46,023 via KBB)
-Was 13.2% more efficient than its EPA estimate
-Traveled 393 miles on a charge despite its 363-mile EPA range https://t.co/Grov2hXqpa pic.twitter.com/Zl8rnZZLIB
— TESLARATI (@Teslarati) June 8, 2026
The Model 3 Rear-Wheel-Drive might be the best bang-for-your-buck EV if you’re looking to buy new and want access to features like Full Self-Driving, while also being aware of efficiency. This trim of the Model 3 is also priced over $9,000 cheaper than what Kelley Blue Book says the average transactional price for a new car was in May 2026, which sits at $46,023.
If you’re looking for something with more speed, an All-Wheel-Drive drivetrain, or more premium features, the Premium trims of the Model 3 currently come with one year of Free Supercharging.
Tesla tipped its hand at where Robotaxi is heading next
Tesla just did something in South Korea that no foreign carmaker has ever done
