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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.
Don’t hesitate to contact us with news tips. Just send a message to simon@teslarati.com to give us a heads up.
Tesla announced on Monday that the Roadster event scheduled for today would be postponed due to the need for it to be held outside.
Less than 24 hours later, CEO Elon Musk broadened that by stating it was due to high winds, immediately sending everyone into a frenzy over the Roadster’s potential ability to fly.
And realistically, it could definitely have to do with it flying, hovering, or hopping; whatever Tesla has in mind for this demonstration could not be impacted by wind. However, it might have nothing to do with the vehicle flying whatsoever, and instead could be a simple precaution, as the Roadster is a very unique vehicle with some already official specs that are just mind-blowing.
Tesla will very, very likely be showcasing both the acceleration rate and potentially even a top speed demo at the event in Waco. Both of these demonstrations, performed with a vehicle that has such incredibly fast metrics, could easily be impacted by wind as well.
Tesla Roadster event requires restricted airspace, and the FAA obliges
Top Speed Demo
At high speeds, aerodynamic forces are already overwhelmingly present. A crosswind or sudden gust adds a layer of sideways force that the tires must counter with slip angle. On a short demo course, that force can shove the car off the intended line, especially in a light car with a low frontal area and little mass to resist the push.
Electric cars, due to their battery packs, have an advantage of an extremely low center of gravity, giving them extra stability. However, the speeds at which the Roadster could travel at the demo could spell some issues if crosswinds are present.
Gusts are worse than a steady wind because the load changes faster than a driver can smoothly correct. That shows up as weaving or a late correction. Headwinds and tailwinds can also spell disaster. Headwinds cut a measured top speed but raise the power needed to get there or maintain it. Meanwhile, a tailwind can inflate the top speed, and downforce issues could become more noticeable.
Wind also loads the body unevenly. A low car can feel light on the upwind side or see a sudden change in downforce if the gust hits a wing or diffuser at an angle. Tire temperature and pressure might stay near a normal level, but lateral grip can be lost as the vehicle is spent fighting the wind.
Acceleration Demo
Launch and 0-60 MPH runs are shorter, so the car spends less time exposed to forces that could cause things to go awry. However, the first second is very sensitive, as a crosswind at launch could yaw the car before speed builds and prior to aerodynamic impact being too great. The driver will be required to correct traction control or manage how much the wheels are spinning, which will likely be corrected automatically by some sort of traction control system within the Roadster (we are fairly certain Tesla will implement something brilliant with it).
These things could cause an unstable run.
A headwind would increase drag as speed rises, while a tailwind would do the opposite. Meanwhile, surface effects, like wind-driven dust, light debris, or even rain, could reduce grip at the exact moment the tires are asked for peak longitudinal force. Standing water plus a crosswind is a common reason an acceleration attempt might be scrapped.
Flying or Not
No matter what Tesla has in store for the Roadster, waiting for ideal conditions is a great idea. People who follow and support the company, along with the engineers involved in the Roadster program, have been waiting nine years since the last unveiling for this moment. Everything should be ideal.
Some speculate that it’s just not ready, and that’s ridiculous. Why would Tesla even schedule the event — albeit prematurely — after nine years if it was not ready? Why would they jump the gun now?
We were all excited for today, but it truly is the most ideal thing in the world to wait two more weeks so everything, including the weather, can be perfect. The delay is simply worth it. But Tesla, seriously, make this the last one.
Elon Musk
SpaceX nails “Lucky 13” astronaut launch, leaning into Tesla tradition and superstition
SpaceX launched Crew-13 astronauts to the ISS Thursday, setting up a record fast Dragon docking.
SpaceX launched NASA’s Crew-13 mission to the International Space Station on Thursday morning, getting four astronauts to orbit despite a forecast of thunderstorms and gusty winds that had threatened to push the flight to Friday.
Falcon 9 lifted off from Space Launch Complex 40 at Cape Canaveral Space Force Station at 11:10 a.m. ET carrying Dragon Grace, NASA confirmed. On board are NASA commander Jessica Watkins, NASA pilot Luke Delaney, Canadian Space Agency astronaut Joshua Kutryk and Roscosmos cosmonaut Sergey Teteryatnikov. The first stage booster, B1101, landed at Landing Zone 40 beside the pad on its third flight after previously supporting Crew-12 and a Starlink mission.
Liftoff of Crew-13! pic.twitter.com/vteT0DXMTh
— SpaceX (@SpaceX) October 1, 2026
It was the first spaceflight for Delaney, Kutryk and Teteryatnikov. Watkins, who flew on Crew-4 in 2022, became the first NASA astronaut to launch aboard a Crew Dragon twice.
Before launch, the crew rode to the pad in Teslas, a tradition on NASA’s SpaceX crew flights since 2020. This time the cars carried specialty plates reading “Lucky 13.” Watkins said the mission patch leans into the number on purpose, as a nod to Apollo 13 and the resilience of that crew.
Grace is now on a short trip to the station. Docking at the forward port of the Harmony module is scheduled for about 7 p.m. ET, roughly 7 hours and 50 minutes after liftoff, which Space.com notes would be the fastest Crew Dragon transit to the ISS yet. Most Dragon flights take around 15 to 24 hours to catch the station. Hatch opening is planned for 8:25 p.m. ET.
The launch came more than two weeks later than originally planned. An oxidizer leak was found in Grace’s propulsion system in August, and NASA and SpaceX added time for tests. That pushed back the return of Crew-12, which has been aboard the station since February and is now set to splash down off Southern California next week. Crew-13 is expected to stay about six months.
SpaceX rescue mission for stranded ISS astronauts nears end — Here’s when they’ll return home
SpaceX already holds NASA orders for crew rotations through Crew-17, while Boeing is preparing an uncrewed Starliner flight to the station as early as December.
Crew-13 was only the first of three SpaceX launches planned for Thursday, as Teslarati previewed on Wednesday. A Falcon 9 launched its Transporter-18 mission from California today, where Google will be launching its first orbital artificial intelligence (AI) test satellite. Meanwhile, Falcon Heavy is set to launch the classified NROL-97 mission for the National Reconnaissance Office from Launch Complex 39A at 11:53 p.m. ET. Its two side boosters will return to Landing Zones 1 and 2, which means Central Florida could hear up to three sonic booms in a single day. The busy stretch follows Starship’s Flight 14 on Monday, which reached orbit for the first time.
Tesla has moved its Wireless Charging efforts for its electric vehicles forward, as it had a new patent published today, one that it submitted back in March.
The patent describes a system for detecting foreign objects on the wireless charging pad under varying temperatures, aiming to mitigate any undesired results that could come from something being on top of the charging pad.
🚨 Tesla has a new patent application published today, which was submitted back in March, for a Wireless Charging Pad:
“The present disclosure relates to methods and systems that can reliably detect foreign objects on a wireless charging pad under varying temperatures. In some… pic.twitter.com/LIeLfZAuDJ
— TESLARATI (@Teslarati) October 1, 2026
The abstract of the patent states:
“The present disclosure relates to methods and systems that can reliably detect foreign objects on a wireless charging pad under varying temperatures. In some examples, an object detector can utilize a set of inductive coils included in resonant tanks, and excite the resonant tanks using signals in a range of frequencies including or near a nominal resonant frequency of the resonant tanks. The object detector can detect a metal object based on resistance of a coil increasing and inductance of the coil decreasing. By analyzing the shifts and/or distributions in resonant frequencies and output magnitudes (e.g., output voltage peaks), the object detector can distinguish between changes of frequencies and magnitudes caused by temperature and those caused by foreign objects to accurately detect the foreign objects.”
The object detection system will utilize a set of inductive coils included in resonant tanks, and “excite the resonant tank using signals in a range of frequencies including or near a nominal resonant frequency of the tanks.” Metal can be detected by an increase in the coil’s resistance and a decrease in the coil’s inductance.
By analyzing shifts or disruptions in resonant frequencies and output magnitudes, the system can detect foreign objects. These types of safeguards need to be implemented through the normal operation of the charging pads.
Tesla says its Cybercab wireless charging efficiency is ‘well above 90%’
Tesla plans to utilize wireless charging with Cybercab and Robotaxi-enabled units to help streamline the fully autonomous experience from A to Z. The last thing the company wants to do is have any sort of small obstruction preventing the rider from experiencing Robotaxi as intended.
Why Tesla Roadster unveiling delay might have nothing to do with it flying
SpaceX nails “Lucky 13” astronaut launch, leaning into Tesla tradition and superstition
