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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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Elon Musk
Elon Musk rips ABC News over fatal NYC Tesla crash report
Musk pushed back on NYC Tesla crash coverage, pointing to a pattern of premature blame.
Elon Musk pushed back overnight against media framing of a fatal Tesla crash in Midtown Manhattan, telling a user on X that “it wasn’t the car” and that the vehicle’s Autopilot system had nothing to do with the wreck.
The crash happened just before 3 a.m. Wednesday, when a 2024 Tesla Model Y struck a sidewalk shed outside 315 Madison Ave., a bus stop pole and a mailbox on East 42nd Street, according to the NYPD. The car kept moving several more blocks before stopping near Second Avenue. Both women inside, each 27, were taken to Bellevue Hospital, where the passenger was pronounced dead. The driver was charged with vehicular manslaughter, driving while ability impaired and leaving the scene of an accident.
Police have not attributed the crash to Autopilot or Full Self-Driving in any public statement. The charges point to impairment, not software. Musk’s response followed a since-deleted ABC News post that he said mischaracterized the incident. Replying to a user on X, Musk wrote that if Autopilot had been engaged, “they would not have crashed,” and added that “the legacy media will never forgive Tesla for failing to advertise with them,”
The legacy media will never forgive Tesla for failing to advertise with them
— Elon Musk (@elonmusk) September 9, 2026
It’s a familiar cycle for Tesla. In June, headlines from several national outlets described a fatal crash in Katy, Texas, as happening while the car was “on autopilot,” based on the driver’s own account to police after his Model 3 struck a home and killed a 76-year-old woman. Tesla’s data told a different story when Ashok Elluswamy, Tesla’s head of AI, said the driver had pressed the accelerator to 100% and reached 73 mph in a residential zone. Harris County prosecutors later confirmed the human override and the driver was charged with manslaughter.
Florida Gov. Ron DeSantis pointed to that same Katy crash last month to argue that outlets routinely name Tesla in crash headlines while leaving other automakers unnamed, even after a driver’s own actions are shown to be the cause. A similar pattern played out in 2024, when Musk had to clarify that FSD was never even downloaded onto the Model 3 involved in a fatal Colorado DUI crash, despite a passenger’s claim that an “auto drive feature” was in use.
Tesla has not issued a separate statement on the Manhattan crash beyond Musk’s posts on X. The NYPD’s investigation is ongoing, and no cause for the driver losing control has been released.
Tesla’s two recently-defunct flagship models, the Model S and Model X, are getting a big upgrade, according to the company’s Head of AI, Ashok Elluswamy.
Older Hardware 3 Model S and Model X vehicles have been the last major holdouts in Tesla’s Full Self-Driving v14 Lite rollout, and that wait now appears to be ending.
Tesla brings closure to flagship ‘sentimental’ models, Musk confirms
At Tesla’s Cybercab launch, AI chief Ashok Elluswamy told Ryan McCaffrey that he thought the S and X build “was supposed to go out last week.” Evidently, Elluswamy expects the suite to be rolled out to those HW3 Model S and Model X very soon:
For my @Tesla friends – and specifically Model S & X owners with HW3 who are waiting on FSD v14 Lite – I spoke to @aelluswamy at the Cybercab launch, & when I asked for an ETA on v14 Lite for S/X owners, he said, “Oh, I thought it was supposed to go out last week.”
So: soon! 🙌
— Ryan McCaffrey (@DMC_Ryan) September 7, 2026
Those cars are not the current Model S and Model X, which already ship with Hardware 4. They are the pre-refresh flagships built around Tesla’s older Autopilot computer, often called HW3 or AI3.
Tesla stopped putting that computer in new vehicles years ago, which is why owners treat these S and X cars as a closed generation. Model 3 and Model Y vehicles on the same computer began receiving v14 Lite in late June 2026 and saw a wider North American expansion in July. South Korea followed as an early international market. The S and X versions of the same software never joined that wave.
v14 Lite is Tesla’s way of squeezing the current v14 driving stack onto hardware that cannot run the full AI 4 model. The company describes the process as distillation: behaviors learned on the newer computer, including reinforcement learning and offline models, are compressed so the older chip and cameras can use them as a guide.
Early descriptions put the distilled network at roughly 15 percent of the original size. The result is still supervised Level 2 driving. Tesla has been clear that HW3 cannot support unsupervised Full Self-Driving or robotaxi operation because of memory and bandwidth limits.
The feature list is what made the wait so frustrating for S and X owners, as plenty of new features are to be shipped with it.
Official notes for the first Lite build, firmware 2026.20.5.1, added parking, unparking, and reversing; arrival options for a parking lot, street, driveway, or curbside; speed profiles that stay available at all times; and start-from-park engagement. Tesla also claimed better handling of merges, forks, pedestrians, traffic lights, and cut-ins, plus fewer false slowdowns and smoother lane centering.
A mid-July follow-on build, 2026.20.6.10, added more of the Hardware 4 interface, including a standalone Self-Driving app and the ability to start a trip from Park without a brake-pedal confirmation.
Elluswamy called that version the one “likely going to wide release.”
That wide release already reached most other HW3 cars in the United States and Canada. International timing still depends on regional validation and regulatory approval. For S and X owners, the remaining work appears to be model-specific validation rather than a new software stack.
There is no official Tesla changelog or build number for those two models yet, only Elluswamy’s offhand timeline. Some HW3 drivers who already have Lite report large gains over v12.6; others have described new indecision or phantom braking. The next test will be whether the same software lands cleanly on the older flagships that have waited the longest.
Yesterday, we reported on Tesla Cybertruck getting some major adjustments from a manufacturing standpoint in an effort to make the all-electric pickup more cost-effective, more reliable, more serviceable, and more easily produced.
Tesla Cybertruck engineer reveals new changes in ‘constantly evolving’ pickup
One of those changes was the addition of a self-reinforcing polypropylene aero shield that sits underneath the truck. Previously, Tesla utilized aluminum for this, but the self-reinforcing polypropylene was more durable while also being cheaper and lighter.
Tesla has revealed another small change it made to the Cybertruck, and it has to do with the side repeater cameras.
Tesla does not wait for a new model year to improve its vehicles. On September 8, Cybertruck lead engineer Wes Morrill posted side-by-side photos of an updated side repeater camera housing now rolling off the line at Gigafactory Texas.
The triangular camera pod mounted on the front fender looks almost identical at first glance. A closer look reveals a revised contour that uses the air already flowing around the truck to keep the lens clearer in rain and road spray.
The side repeater camera was updated – the version on the left is the newer part which uses passive geometry to create airflow disturbance that better keep water off the lens while driving. No cost penalty, just pure vision improvement. pic.twitter.com/wAbXtcL1Jf
— Wes (@wmorrill3) September 8, 2026
The side repeater cameras sit in an exposed position on the Cybertruck’s angular stainless-steel body.
In wet weather, they readily collect water droplets that can degrade the image Autopilot and Full Self-Driving use for lane changes and blind-spot monitoring. Early production trucks sometimes left owners wiping lenses by hand or accepting temporary restrictions on driver-assistance features.
Tesla has added washers to cameras on certain other models and on Cybercab prototypes, but those active systems add cost, complexity, and extra potential leak points.
The new housing solves the problem with passive geometry. Subtle changes in the surround create localized airflow disturbances as the vehicle moves. Those eddies physically push water droplets away from the optical surface. Morrill called the result “pure vision improvement” achieved at “no cost penalty.” Once the production mold is updated, every subsequent part costs the same as the original.
The advantages compound quickly. Clearer cameras in rain improve the reliability of driver-assistance features precisely when they are needed most. The design consumes no extra energy and introduces no new failure modes.
New Cybertrucks built after the tooling changeover receive the updated part automatically. Some owners of trucks delivered as late as June 2026 have already confirmed they received the revised housing. Retrofit questions have appeared in replies, and the cameras appear electrically compatible, though Tesla has not announced an official service program.
A few millimeters of reshaped housing will not make headlines the way a new battery pack does, but these changes are incremental and increase the Cybertruck’s effectiveness as a vehicle over time.
This improvement illustrates how Tesla continues to refine the Cybertruck after volume production began. Better wet-weather vision, zero added cost, and no extra hardware add up to a meaningful gain in everyday usability and safety.
Elon Musk rips ABC News over fatal NYC Tesla crash report
Tesla’s two defunct flagship models are getting a big upgrade
