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Tesla FSD Beta 10.69 release notes highlight better left turns, smoother driving
Tesla released FSD Beta 10.69 to the first round of testers over the weekend. Read v.10.69’s release notes below to check out the latest improvements.
Stay in your Lanes
- 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 connectivites. 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.
Nothing Like Smooth Driving
- 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 manevuers.
- 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.
- 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.
- Reduced latency when starting from a stop by accounting for lead vehicle jerk.
Chuck’s Left Turn
- 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 optimizable 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.
Safety is Number 1
- 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.
- Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects.
- Improved speed when entering highway by better handling of upcoming map speed changes, which increases the confidence of merging onto the highway.
- Enabled faster identification of red light runners by evaluating their current kinematic state against their expected braking profile.
Tesla FSD “Brain” Improvements
- 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.
- 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.
- Improved recall of animals by 34% by doubling the size of the auto-labeled training set.
- 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.
Tesla is rolling out FSD Beta v.10.69 in phases, starting with ~1,000 testers over the weekend. Once the update is rolled out for wide release, the price of FSD Beta will increase.
The Teslarati team would appreciate hearing from you. If you have any tips, contact me at maria@teslarati.com or via Twitter @Writer_01001101.
Tesla Vice President of Vehicle Engineering, Lars Moravy, recently revealed the company has thought about introducing a Plaid powertrain on the Model 3, but there could be some challenges involved.
On the Ride the Lightning podcast, Moravy revealed that he thinks about a Plaid Model 3 “all the time,” and it certainly has a place in Tesla’s potential lineup of future vehicles.
Now that the Plaid powertrain is technically defunct due to the newfound absence of the Model S and Model X, Tesla could find a way to reintroduce the lightning-quick trim level to its mass-market vehicles.
But there are going to be some challenges with it. Moravy said that the Model 3 Plaid would likely adopt the carbon-sleeved motors that the Model S Plaid had. However, packaging would be a major challenge, as Moravy said on the podcast, it would be a “tight engineering squeeze.”
It’s important to note that there are no active production plans for the Model 3 Plaid at this point, but it’s also worth noting that with the Model S and Model X Plaid no longer available, Tesla would likely be willing to introduce something that is even more white-knuckle than the Model 3 Performance, which already boasts a 2.9-second 0-60 MPH acceleration rate and a top speed of 163 MPH.
Of course, there is the Roadster, but we don’t know when that will exactly make it to market, and we know that, for sure, it will not be accessible to many.
Tesla unveils juicy new detail on the Roadster and hints at new unveil timeline
Tesla has prided itself in building some of the best cars out there, but they’re also interested in building cars that are simply fun to be in.
A Plaid Model 3 could truly push the limits and could end up being one of the best cars Tesla will ever build, especially if it can shave off at least half of a second from its 0-60 MPH time and increase its top speed slightly.
More than anything, the real changes will be in the ride and aerodynamics. Tesla improving things like the suspension, handling, and downforce will be the true trademarks of its Plaid powertrain; putting it in the Model 3 could be a great move for the company and for customers interested in high-end performance.
Elon Musk
NASA’s first human outpost on the Moon starts now – SpaceX on deck
NASA named the rovers, landers, and vendors that will build America’s first Moon Base.
NASA has laid out its most detailed Moon Base plan to date, describing a permanent outpost near the Moon’s south pole that the agency intends to build over the coming decade as a direct stepping stone to Mars. “The Moon Base will be America’s and humanity’s first outpost on another celestial world,” NASA Administrator Jared Isaacman said, adding that every mission crewed and uncrewed “will be a learning opportunity as we return to the lunar surface, build the infrastructure to stay, and master the skills required to live and operate in one of the most demanding and dangerous environments imaginable.”
The plan is structured in three phases involving both uncrewed and crewed missions to deliver equipment, vehicles, and infrastructure to the surface, with the first three moon base missions targeted to launch before the end of 2026.
Moon Base I, targeting fall 2026, will use Blue Origin’s Blue Moon Mark 1 lander to deliver scientific instruments to the Shackleton Connecting Ridge, the same region where Artemis astronauts will land. Moon Base II will send Astrobotic’s Griffin lander carrying more than 1,100 pounds of cargo including Astrolab’s FLIP rover to begin developing mobility systems on the surface. Moon Base III will carry the Lunar Vertex science mission on Intuitive Machines’ Nova-C Trinity lander to study lunar swirls near the south pole, with ESA and Korean science payloads aboard.
On the rover side, NASA awarded Astrolab $219 million and Lunar Outpost $220 million to build the first phase of Lunar Terrain Vehicles, with both rovers targeted for deployment to the lunar surface by 2028. Astrolab’s crewed rover weighs roughly 2,000 pounds and can reach over 6 mph. Lunar Outpost’s Pegasus rover can operate autonomously or via remote control at over 9 mph. Blue Origin separately received $188 million with an option worth $280.4 million to deliver cargo landers for rover transport.
NASA also confirmed that MoonFall, a mission deploying four survey drones to scout Artemis landing sites, has selected Firefly Aerospace to build the transport spacecraft, with a 2028 launch target.
SpaceX sits at the center of that commercial layer. SpaceX holds the NASA Human Landing System contract for the Starship-derived lander that will put astronauts on the surface under Artemis IV, currently targeting 2028. Before that can happen, SpaceX must demonstrate in-orbit propellant transfer at scale, a process requiring multiple Starship tanker launches to fuel a single mission. Water ice at the lunar south pole is central to the base’s long-term viability, as it can be converted into drinking water, breathable oxygen, and rocket fuel, directly reducing dependence on Earth resupply. That resource loop becomes far more practical if Starship can land and be refueled on or near the Moon itself.
Elon Musk has publicly stated that Starship V3, which recently completed its first flight, should be capable enough for initial Mars missions. The Moon Base plan announced Tuesday is the infrastructure layer that connects everything between those two ambitions, and SpaceX is the only American company currently contracted to build the rocket that gets humans to either destination.
A new Tesla patent that has been granted to the company this week has revealed a potential strategy for solving a major issue that could impact both the Full Self-Driving suite and Optimus.
The patent, which is No. 12,636,684, describes a “Lens Cleaning System,” and was submitted by Tesla in May 2025.
The language in the patent details a lens cleaning system that can dispense fluid and wipe it away with a wiper assembly.
Optimus can see you now… 🤖👁️
The patent for @Tesla_Optimus‘s eye structure just dropped. $TSLA pic.twitter.com/Jac4VhDmKH
— SETI Park (@seti_park) May 26, 2026
This would effectively clean any debris that would potentially impact the visibility of the cameras on Tesla automobiles or Optimus’s camera eyes. Perhaps the most pertinent example is through the Full Self-Driving suite, as debris that can accumulate on the vehicle’s exterior cameras can impact the suite’s ability to operate effectively.
This requires a remedy through manual cleaning, but this patent hints that Tesla could be planning to implement this new technology on its upcoming vehicles.
Interestingly, we have started to see it on some Robotaxi vehicles, and it will likely be included in the Cybercab, especially as that vehicle will enable full autonomy.
Back in January, the first Model Y Robotaxi units were spotted with camera washers on the side repeaters, as the video below shows fluid squirting and rinsing off any debris that is limiting visibility.
🚨 Tesla looks to have installed Camera Washers on the side repeater cameras on Robotaxis in Austin
pic.twitter.com/xemRtDtlRR— TESLARATI (@Teslarati) January 23, 2026
This hardware patent does bring up an interesting question for those of us who own Teslas with AI4 and have been told that our cars will one day be capable of full autonomy: Will this washer be available as a retrofit on already-built cars?
Perhaps the “Lens Cleaning System” patent is a good look at one way Tesla plans to combat one of the most obvious issues of autonomy that utilizes a camera-based system. For Optimus, it could be less needed as it could be manually cleaned by owners. For cars, it seems like a bigger necessity, especially as autonomy nears and Tesla gets close to launching a feature-complete FSD suite.
Tesla teases going Plaid Mode with the Model 3
NASA’s first human outpost on the Moon starts now – SpaceX on deck
