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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.
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Tesla Cybercabs take over Austin’s F1 weekend
Tesla Cybercabs swarmed Austin’s F1 Grand Prix as sightings hit a record during race weekend.
Tesla’s driverless Cybercabs used the Formula 1 United States Grand Prix in Austin as their biggest public stress test yet. On Sunday afternoon, Tesla head of AI software Ashok Elluswamy quoted a photo from X user Ben Steindl showing a long line of gold Cybercabs and Model Y Robotaxis stacked up on a road near Circuit of the Americas. “Robotaxis as far as the eye can see!” Elluswamy wrote. Steindl’s original post was more matter of fact: “@Tesla @robotaxi is busy for the Austin F1.”
The fleet also handled the trip home. The race started at 3 p.m. Central, and once it ended, Cybercab Spotter, an account that logs Cybercab sightings around Austin, posted that “People are requesting Cybercabs to get home.”
Robotaxis as far as the eye can see! 🤩
— Ashok Elluswamy (@aelluswamy) October 25, 2026
Tesla set this up days in advance without announcing it. Late on October 22, rider David Moss posted Robotaxi app screenshots showing the Austin service area wrapping around the track toward Elroy and FM 812, with ride requests open until 1 a.m. A banner in the app read “Last call is 1am · Limited to nearby dropoffs.” Austin rides ended at 11 p.m. as recently as early October, as Teslarati reported, before moving to midnight on October 17.
Not every rider got dropped at the gate. Early app searches showed spots like COTA Karting as “Service blocked,” and one rider who took a Cybercab to the track Friday said the rideshare area was less than a 20 minute walk from the front gate.
The weekend also produced a new activity record. Cybercab Spotter counted 1,250 Cybercab sightings in Austin on Saturday, qualifying day. The previous high was 1,072 on October 16. Sightings count the same car more than once, so they measure how busy the fleet is rather than how big it is. For size, the Texas automated vehicle registry listed 463 Cybercabs at midday Sunday, up from the 300+ Tesla claimed on October 10, when Elluswamy also said Dallas is next for Cybercab.
The track was out of reach for Tesla’s main rival. Waymo runs in Austin only through Uber, and its service area stops well short of COTA. Waymo did not announce any race day service.
Tesla has not said how many rides it gave to and from the circuit, or whether the expanded zone and 1 a.m. cutoff will stay after race weekend. On the third quarter earnings call, Elluswamy said 2 a.m. service is coming “pretty soon” and that Tesla plans to run Robotaxi around the clock before the end of the year. The company is also aiming to hand the first Cybercabs to customers before 2026 ends, which would put cars like these outside Tesla’s own fleet for the first time.
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Tesla makes good on its Cybercab promise in just two weeks
Tesla Cybercab riders can now pull up a rear camera view and darken the screen.
Tesla has given Cybercab passengers a way to see what is behind them, along with a way to turn the cabin screen off entirely.
Riders in Austin can now bring up a live rearview camera feed on the Cybercab’s center display in the middle of a trip. Austin rider Ethan McKanna spotted the change late Friday night, posting from a ride that “You can now open the camera to view what’s behind you in Cybercab!” His photo shows a strip of rear camera video across the top of the screen, with another Cybercab following on a bridge. Sawyer Merritt shared the image Saturday and wrote that it “sounds like” there is also a new quiet mode that darkens the entire screen for riders who want to relax, sleep, or avoid a bright display at night.
The camera view goes beyond the rear feed. Tesla UI software engineer Evan, who posts as @CarCoder, replied to McKanna with a tip: “You can also tap it to see more cameras, and swipe it up or down to hide and show.” Until now, the Cybercab only showed its rear and repeater feeds on the “Please Exit Safely” screen at the end of a ride, so passengers could check for cyclists and traffic before opening a door.
Quiet mode surfaced a day earlier. Austin rider Jackson Blatz posted a video Friday morning of the new Robotaxi interface in a Cybercab with the option. Tesla has not published release notes for either feature.
The rear camera view moved quickly from request to release. Earlier this month, Cybercab rider Dan Burkland asked Tesla to let passengers pull up the rear and repeater cameras whenever they want, “especially for peace of mind.” Evan answered “In development,” as Teslarati reported at the time. About two weeks later, riders have it.
The feature fills a gap created by the Cybercab’s design. With no steering wheel, no pedals, no mirrors and no rear window, a passenger has no natural way to glance behind the car. The eight exterior cameras that feed Tesla’s autonomy software were already on board, so the change only required software.
There is also an AI angle to how it was built. Yun-Ta Tsai, a senior staff engineer on Tesla’s AI team, quoted Merritt’s post and wrote that the “(human) camera view tuning is optimized with Grok and implements part of the algorithm by directly programming the fixed-function hardware, so I don’t even need to check the datasheet.” Tsai did not say which part of the algorithm Grok handled. Grok already plays a bigger role inside the vehicle, with Grok Bot now enabled across every Cybercab and the Model Y Robotaxi fleet after Tesla brought the agent into its cars in September.
The two features join a busy week of changes for riders in Austin, which also brought a redesigned Live Activity for iPhone and Apple Watch and live trip sharing. They also arrive as Tesla prepares the Cybercab for life outside its own fleet. On the third quarter earnings call, Lars Moravy said the company is aiming to deliver Cybercabs to customers before the end of 2026. Tesla has not said whether the camera view and quiet mode will also come to its Model Y Robotaxis.
Elon Musk
Tesla shows what Roadster drivers will see on screen before thruster activation
Tesla’s Roadster video reveals a fighter jet inspired cockpit with a 5,500 PSI thruster gauge.
Tesla has given future owners their clearest look yet at the cockpit of its next generation Roadster. In a 34 second video posted to X on Friday afternoon, the company walked through the car’s touchscreen with a caption: “Inspired by fighter jet cockpits, Roadster’s interface was designed around the pilot.”
The clip opens on a black screen with red graphics and a 3D wireframe of the car, which sits beside the Roadster’s squared off yoke. Tapping parts of the model brings up controls for the rear wing, including low drag and high downforce positions, along with a charging page with a charge limit bar and a small music player. A row of tabs along the bottom reads Home, Vehicle, Listen, Climate and Navigate.
Most of the video is spent in Track Mode. The dashboard shows a live circuit map, a list of session lap times, brake and motor temperatures, and pressure and temperature readouts for each tire. A setup page lets the driver pick Auto, Low Drag, High Downforce or Custom aero modes, adjust aero balance and brake balance, and switch stability and traction control off. Along the bottom, a status bar reads “Thruster · 5,500 PSI” next to a “Boost Ready” indicator.
That gauge ties the software to the hardware Tesla showed off near Waco, Texas on October 15. The Thrust and Rocket versions use cold gas thrusters fed by carbon overwrapped pressure vessels, and Tesla lists 0 to 60 mph times of under 0.9 seconds and under 0.59 seconds for them, as Teslarati detailed in its breakdown of the three Roadster configurations. The base Performance car relies on a fan and ground sealing system for grip, and the aero settings on screen match the active wings and suction setup covered in our look at how the Roadster generates downforce.
The Roadster layout is a departure from the rest of Tesla’s lineup. Instead of a map centered screen, the Roadster puts the car itself at the center of the home page, and Tesla’s Roadster page lists two smaller forward displays alongside the main touchscreen. Tesla has not said whether any of the Track Mode telemetry will make its way to the Model 3 Performance or other vehicles.
The interface could still change before deliveries. Elon Musk said at the event that Roadster production will begin in 2028, and Tesla says the flagship Rocket version is not approved for public road use. Reservations are open with a $50,000 payment, while Thrust is listed as a limited release and Rocket is invite only.
Those who want to see the car in person will get their first chance this weekend. The Roadster goes on display in the lobby of the Petersen Automotive Museum in Los Angeles from October 24 to 27, according to Sawyer Merritt, the first time the public will be able to view it up close.
Tesla Cybercabs take over Austin’s F1 weekend
Tesla makes good on its Cybercab promise in just two weeks
