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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.
Elon Musk is, in many respects, one of the biggest and most influential figures in modern-day artificial intelligence.
Given that Tesla, SpaceX, and xAI are all looked at in their respective fields as leaders to an extent, each of them has a heavy influence on the future of AI, even though two of them are not thought of, at face value, as AI companies.
Musk has grand expectations for what is to come with AI, not only as a form of assistance to make human lives easier, but to make humans multiplanetary and solve some of the biggest issues that face us today. But even he is astounded by AI’s pace of progress.
He believes that in two years, AI will be so mind-blowing it might be unrecognizable.
Given that AI from 2 years ago feels so old that it should be in a museum, then obviously AI 2 years from now will be mind-blowing https://t.co/TcsKZ8o8OE
— Elon Musk (@elonmusk) July 30, 2026
This progress can be seen in a variety of ways, but perhaps the most popular way people have shown AI’s progress, especially on social media, is through an incredibly arbitrary way of watching Will Smith eat spaghetti:
The progression in AI of Will Smith eating spaghetti (2023 – 2026) pic.twitter.com/VDv82mB5gs
— internet hall of fame (@InternetH0F) February 10, 2026
This is a great way to show people how AI is improving, especially from a perspective that examines how it can manufacture images and video from prompts. AI is an incredibly complex concept, however, and it goes much deeper than Will Smith eating Italian food.
Musk’s most widely adopted method of AI is likely Tesla Full Self-Driving, which impacts millions of people as they utilize it to increase safety with their travel. Musk has routinely pushed incredibly aggressive timelines for self-driving, especially unsupervised.
Perhaps this perspective is why he feels that things will be solved in a timeframe that is much more aggressive than most of us would think. Regardless, the progress of AI is moving fast, and it seems that Musk’s expectations for it could be high.
But if it can actually achieve full-length motion pictures and even more realistic production value, it will be hard to distinguish between reality and AI very soon.
Tesla just officially confirmed it has built its 10 millionth car, a major milestone for the company that started producing sustainable electric powertrains less than two decades ago.
In that time, Tesla has truly revolutionized the automotive industry, disrupting the idea of what a car should be, how it should be fueled, and how it truly impacts day-to-day life.
10 million vehicles produced globally.
Congrats to all Tesla teams! pic.twitter.com/JkcraR63bs— Tesla Manufacturing (@gigafactories) July 30, 2026
Tesla achieved this feat across four production facilities: the Fremont Factory in Fremont, California, Gigafactory Shanghai in China, Gigafactory Berlin in Germany, and Gigafactory Texas in Austin, Texas.
The 10 millionth vehicle was a Diamond Black Model Y.
Over the course of the past roughly 18 years, Tesla has evolved its lineup from a sporty sedan built on a Lotus body to a lineup of various body styles, performance metrics, and other characteristics that make each one unique.
This is an incredible achievement for a company that is young compared to what it goes up against. When Tesla entered the automotive market, Ford, GM, and Stellantis widely dominated the playing field. Since then, Tesla has caused such a disruption that these three massive brands had to scramble to create EV projects of their own.
Despite their best efforts, they have not been able to match the prowess or the effectiveness of Tesla. They are all reliant on Tesla’s charging infrastructure, their software is inferior, and their self-driving projects are elementary in comparison.
Tesla felt its fair share of growing pains over the years as well. As recent at 2019, there were complaints about build quality, paint quality, and overall luxuriousness. These things have all been improved upon through the company’s maturity, and these strides in quality have led to this 10 million vehicle production achievement, something that other small-and-scrappy EV makers will hope to accomplish one day.
SpaceX just picked up another $1.6 billion from the Pentagon, with the U.S. Space Force awarding two task orders worth $1.6 billion to fly 18 Falcon 9 missions from Vandenberg Space Force Base in California through the end of 2027. The launches will carry satellites for the Space Based Sensing and Targeting portfolio, a set of programs meant to help the military detect and track airborne threats and relay that information across forces in near real time.
The award falls under National Security Space Launch Phase 3 Lane 1, the Space Force’s faster, commercial style procurement track for missions that do not require the military’s most demanding certification process. It is also the largest single order publicly disclosed under that program so far, and the first task order issued since the Space Force nearly tripled Lane 1’s contract ceiling from $5.6 billion to $17 billion on July 17.
SpaceX to become America’s Military data backbone for missiles, drones, and warfighters
Eric Zarybnisky, the Space Force’s acting portfolio acquisition executive for space access, said the entire process, from identifying the requirement to signing the contract, took about two months, including a month set aside for companies to prepare proposals.
SpaceX is not just launching these satellites. It already holds the contracts to build two of the programs within the same portfolio, $4.16 billion for the Space Based Airborne Moving Target Indicator system and $2.29 billion for the Space Data Network Backbone, which Teslarati covered in May. That means SpaceX is now responsible for both building key pieces of the military’s next generation sensing network and getting them into orbit.
With this latest award, SpaceX’s Pentagon contract total for 2026 alone tops $8 billion, adding to a defense portfolio that already includes the Golden Dome missile defense software group SpaceX joined in April and a string of GPS launches it inherited after ULA’s Vulcan rocket ran into a booster anomaly, which we detailed in March.
Lane 1’s vendor pool technically includes seven companies: SpaceX, ULA, Blue Origin, Rocket Lab, Stoke Space, Impulse Space, and Relativity Space. In practice, SpaceX remains the only provider with the combination of launch cadence, flight proven Falcon 9 hardware, and West Coast infrastructure to support a campaign requiring roughly one Vandenberg launch a month for the next year and a half.
Some lawmakers have flagged the growing concentration of national security launches with one company as a risk worth watching. For now, the Space Force keeps backing SpaceX, with it being the company that shows up ready to launch.
Elon Musk has a crazy prediction about AI in two years
Tesla just built it 10 millionth car
