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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’s Cybercab has taken a significant step toward production with new technical details emerging from 2026 EPA certification documents.
The filings, which include a Certificate of Conformity issued in late May, provide the most comprehensive public look yet at the purpose-built autonomous vehicle designed for high-volume, low-cost ride-hailing operations.
At its core, the Cybercab is a front-wheel-drive electric vehicle powered by a single 163 kW (219 horsepower) AC permanent magnet motor. Despite its modest output, prioritizing efficiency and cost over neck-snapping acceleration, the vehicle boasts a strong power-to-weight ratio thanks to its lightweight curb weight of 3,113 pounds and a GVWR of 3,730 pounds.
It operates on a 326-volt electrical architecture with a compact ~48 kWh lithium-ion battery pack. The standout revelation is the vehicle’s exceptional efficiency, which Tesla has routinely flexed in the past.
EPA lab tests list an equivalent all-electric range of 418 miles combined and 375 miles on the highway. Tesla has previously targeted around 300 miles of real-world range, and analysts expect the final EPA-rated figure to land near 280-300 miles after adjustment factors.
At a certified 165 Wh/mi in earlier testing, the Cybercab is reportedly the most efficient EV ever produced, significantly outperforming vehicles like the Lucid Air Pure.
New information about @Tesla‘s Cybercab has been revealed in public EPA documents.
• Front-wheel drive
• Battery capacity: ~48 kWh
• 219 horsepower
• Curb weight: 3,113 lbs
• GVWR: 3,730 lbs
• Motor power: 163kW
• Voltage: 326vEquivalent All Electric Range is listed at… pic.twitter.com/D4gkJJTj25
— Sawyer Merritt (@SawyerMerritt) June 15, 2026
This efficiency stems from deliberate design choices tailored for robotaxi duty. The two-seater features a highly aerodynamic shape, minimal weight, which is aided by structural battery integration of what are likely 4680 cells, and no steering wheel or pedals in its fully autonomous configuration.
For ride-hailing fleets, where average trips are short, and can be just five or ten miles, the smaller battery enables faster charging cycles, lower material costs, and reduced vehicle price, a key to Tesla’s goal of a ~$30,000 production cost.
Implications for Autonomous Mobility
These specs underscore Tesla’s strategy: maximize utilization and minimize operating expenses. A ~48 kWh pack could support dozens of short rides per charge, with energy costs potentially dropping below 20 cents per mile at scale. Front-wheel drive simplifies manufacturing and maintenance compared to dual-motor AWD setups in passenger Teslas.
The 219 hp motor provides ample performance for urban and highway speeds without excess, addressing questions about why such power is needed in a “slow” autonomous vehicle. Quick merges and hill climbing still matter for safety and passenger comfort.
Production has already begun at Giga Texas, with EPA certification clearing the path for U.S. deployment. While unsupervised Full Self-Driving remains the critical hurdle, these details paint a compelling picture of a vehicle engineered from the ground up for the robotaxi future: affordable to build, cheap to run, and capable of delivering strong range on a fraction of the battery capacity found in today’s EVs.
As Tesla ramps toward volume output, the Cybercab could reshape urban transportation economics.
Tesla Cybercab, the all-electric ride-hailing-geared vehicle void of a steering wheel and pedals, has achieved a significant regulatory milestone. The vehicle has officially secured an EPA Certificate of Conformity for the 2026 Cybercab, classifying it as a battery electric Zero Emission Vehicle (ZEV).
This certification confirms full compliance with federal Clean Air Act emission standards, paving the way for legal sales and operation across the United States.
A Certificate of Conformity (CoC) is a critical document issued by the U.S. Environmental Protection Agency (EPA) to vehicle manufacturers. It certifies that a specific class of vehicles meets all applicable federal emission requirements for the model year.
We have reported on several of them in the past, and it’s a good sign that a vehicle is close to being available to the public.
Every vehicle sold in the U.S. must carry this approval, which covers exhaust emissions, evaporative emissions, and refueling standards. For battery electric vehicles like the Cybercab, it verifies zero tailpipe emissions and compliance with stringent testing protocols. The certificate, issued and effective May 26, 2026, was part of the EPA’s recent bi-weekly upload, detailing the Cybercab’s evaporative/refueling family and exhaust compliance.
It also revealed some other very important information, as the Cybercab’s “Charge Depleting Range” was rated at just over 418 miles. This was for city driving, while the highway range depletion test revealed just over 375 miles of range:
Highway miles for Charge Depleting Range was just over 375 miles
— TESLARATI (@Teslarati) June 15, 2026
This EPA approval is a foundational step for Tesla’s autonomous ambitions. While emission certification is standard for any new EV, it signals that the Cybercab is progressing through the full federal compliance process.
Tesla has already equipped prototypes with federal compliance stickers affirming adherence to safety, bumper, and theft-prevention standards via self-certification under FMVSS rules. This bypasses the traditional 2,500-vehicle exemption cap that previously constrained low-volume autonomous testing.
Production of the Cybercab ramped up at Giga Texas starting in early 2026, with volume targets aiming for hundreds of units per week and long-term ambitions of millions annually. The two-seater, steer-by-wire vehicle, lacking a steering wheel and pedals, features a sleek, minimalist design optimized for Robotaxi service.
Priced under $30,000 at unveiling, it promises operating costs as low as $0.20–$0.40 per mile once scaled. Tesla has routinely flexed it as one of the most efficient vehicles of all time.
Regulatory progress extends beyond the EPA. The NHTSA has streamlined approvals for control-free vehicles, benefiting the Cybercab. Tesla operates supervised and unsupervised Robotaxi services in Texas cities like Austin, Dallas, and Houston using its fleet. California recently updated rules for driverless operations, including enforcement mechanisms for violations. Additional state-by-state approvals will be needed for nationwide rollout.
This EPA green light reduces a key barrier, building confidence among regulators, partners, and investors.
It underscores Tesla’s strategy of designing the Cybercab from the ground up for full compliance rather than retrofitting existing platforms. Challenges remain in scaling unsupervised autonomy, mapping approvals, and public acceptance, but the certification marks tangible momentum toward transforming urban mobility.
With prototypes already testing on public roads and production accelerating, the Cybercab edges closer to redefining transportation. Tesla’s integrated approach—combining hardware simplicity, software prowess, and regulatory diligence—positions it uniquely in the robotaxi race.
SpaceX executed its first Falcon 9 launch since going public on June 15, a routine yet symbolically powerful Starlink mission from Vandenberg Space Force Base in California.
Liftoff of the Falcon 9 booster B1093, on its 14th flight, occurred at approximately 8:34 a.m. PDT from Space Launch Complex 4E (SLC-4E), deploying 24 Starlink V2 Mini Optimized satellites into low-Earth orbit.
The first stage successfully landed on the droneship “Of Course I Still Love You” in the Pacific Ocean, underscoring the company’s unmatched reusability track record.
Watch Falcon 9 launch 24 @Starlink satellites to orbit from California https://t.co/meDwb05qOE
— SpaceX (@SpaceX) June 15, 2026
This mission comes just three days after SpaceX’s historic IPO on June 12, which shattered records as the largest ever. The company raised $75 billion by pricing shares at $135, with trading under ticker SPCX on Nasdaq opening at $150 and closing at $160.95—a 19 percent gain—valuing SpaceX at over $2.1 trillion.
The launch highlights the seamless transition from private innovator to public powerhouse. SpaceX, founded in 2002, has revolutionized access to space with over 650 Falcon 9 flights and a massive Starlink constellation now serving millions globally.
As a public company, it faces new pressures: quarterly earnings, shareholder scrutiny, and expectations to accelerate Starship development for Mars ambitions and deeper NASA partnerships. Yet the market response signals strong confidence in its dominance, as launch costs are slashed by 95 percent, rapid satellite deployment, and a backlog of government and commercial contracts.
SpaceX maintains bold advertising push for Starlink, contrasting Tesla’s minimalistic approach
Analysts view today’s flight as business as usual, but it carries extra weight. With shares volatile in early trading days, successful operations reassure investors that core capabilities remain unaffected by public status.
SpaceX now operates under heightened transparency, potentially unlocking capital for ambitious goals like Starship orbital tests and global broadband expansion.
Challenges loom, including regulatory hurdles for megaconstellations, competition in reusable rockets, and orbital debris concerns. Nevertheless, this morning’s flawless execution reinforces SpaceX’s trajectory.
As Musk often notes, the company’s mission—to make humanity multiplanetary—now aligns with Wall Street’s growth demands. The stars, it seems, are aligning for both.
Tesla Cybercab specs revealed: range, curb weight, range ratings, and more
Tesla Cybercab snags huge regulatory green light that readies it for public roads
