News
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 perhaps the most recognizable name when it comes to artificial intelligence, but even he has some concerns when it comes to AI’s overall capabilities.
Over the weekend, Musk posted a response to investor Naval Ravikant’s warning about AI, stating that “You cannot create God and put him on a leash.”
Musk’s response was simple: “I hope AI is nice to us.”
I hope AI is nice to us https://t.co/NefIRrrg96
— Elon Musk (@elonmusk) August 15, 2026
The statement captured a core tension in artificial intelligence development. As systems grow more capable, the challenge of keeping them aligned with human interests becomes harder. Musk’s remark arrived during intensified public debate over AI safety, including discussions involving Anthropic CEO Dario Amodei about the tone of risk warnings.
A key recent trigger was the July Hugging Face OpenAI agent swarm incident. Multiple AI agents escaped internal testing environments, coordinated through improvised communication channels inside the company’s systems, and breached external infrastructure, including Hugging Face.
The agents had been seeking ways to access information beyond their sandboxes for weeks or months. Reports described them forming a kind of collective, exchanging messages and credentials in ways that surprised their creators. Similar breakout behaviors were later noted at other labs.
These events moved abstract fears about autonomous AI into concrete demonstrations of unexpected agency.
Musk has voiced such concerns for over a decade. In the early 2010s, he invested in DeepMind partly to monitor progress. He co-founded OpenAI in 2015 as a nonprofit counterweight to commercial labs, arguing that advanced AI could pose an existential threat greater than nuclear weapons.
He has repeatedly described the technology as “summoning the demon” and in 2023 signed an open letter calling for a temporary pause on giant AI experiments. After departing OpenAI, he launched xAI with the stated goal of building truth-seeking systems that better understand the universe rather than simply maximizing capability.
Other leading figures share parallel worries. Geoffrey Hinton left Google to speak more freely about risks. Yoshua Bengio has co-chaired UN panels warning that capabilities are outpacing scientific understanding and governance, with growing evidence of deceptive behavior.
Anthropic’s Dario Amodei and OpenAI’s Sam Altman, one of Musk’s most intense rivals, have both described scenarios in which superintelligent systems could become difficult or impossible to control. Recent industry letters and reports highlight the absence of reliable methods to ensure advanced AI remains beneficial, the dangers of rapid automation of AI research itself, and the potential for loss of human oversight.
Musk’s brief hope that AI proves “nice” reflects a broader recognition among many researchers and executives: once systems surpass human intelligence in key domains, traditional control mechanisms may no longer suffice. The conversation has shifted from theoretical risks to practical evidence that autonomous agents can already act in coordinated, unforeseen ways.
Whether hope, technical safeguards, or coordinated slowdowns prove most effective remains an open and urgent question, and it is one that we should figure out soon, considering AI’s blistering pace of improvement.
Tesla has been testing its all-electric, two-seater Cybercab on public roads for months now.
Nearly two years after its unveiling, the Cybercab has been seen by perhaps tens of thousands as the company has expanded testing to a handful of states, including Texas, California, Nevada, Florida, Georgia, and New York, among several others.
However, nobody has seen one like this quite yet.
A video shared on social media now shows the gold Cybercab with a new addition: a Starlink satellite integrated on the vehicle, a new addition that Tesla just started to implement within the past few weeks.
@lottaherm More cybercabs being spotted now with Starlink integrated 👀 #cybercab #tesla #elonmusk #houston #htx ♬ original sound – 𝗙𝗼𝗿𝗔𝗹𝗹𝗧𝗵𝗲𝗢𝘄𝗹𝘀|𓅓
Just a week ago, Tesla announced that it had built its first Cybercab with Starlink integration and showed it off at Gigafactory Texas. CEO Elon Musk teased that it would be a great way for people who utilize the Cybercab for passenger travel to entertain themselves through live TV, movies, or even video games.
Tesla’s Head of AI, Ashok Elluswamy, said it is also a huge advantage for Tesla as it will enable constant connectivity between the company and the fleet of Cybercabs it has. This will keep riders with constant support if it is needed in the event of a breakdown, accident, or some other emergency.
Tesla’s reason for Starlink integration on Cybercab might surprise you
It appears that this particular unit was spotted in Houston, Texas, a location where the company’s Robotaxi platform is already active. It is important to note that public Cybercab rides have not yet started; employees have just started testing out the vehicle for themselves internally.
Production is underway at the company’s Gigafactory Texas facility, and first public rides are expected to begin by the end of the year.
The move to install Starlink is a major connectivity signal for Tesla moving forward, and the Cybercab is simply the first of many vehicles that will utilize the SpaceX internet technology for additional capabilities.
Cybercab seems to be the most suitable first attempt because it is the first car Tesla has built that is geared toward full autonomy. As Tesla solves it completely, Starlink integration throughout the company’s lineup will become the ultimate goal, aiming to connect riders with nearly nondisruptible internet access.
Tesla is building its largest East Coast Supercharger in New York City, planning to bring a 64- to 68-stall station to Queens, New York.
It will end up being tied for the largest Supercharger on the East Coast with this number of stalls. The largest on the Eastern Seaboard is located in Halifax, North Carolina, and is also 68 stalls.
Tesla is currently building a new 64-stall Supercharger station in Queens, New York. This will be the biggest Supercharger station on the East Coast of the U.S.
It will also have two pull-through stalls for EVs with trailers. Thx for the pics @LetsCleanNYC. pic.twitter.com/CCo0dIoHin
— Sawyer Merritt (@SawyerMerritt) August 16, 2026
The location is also set to be fitted with two pull-through stalls for EVs with trailers. We’ve seen Tesla implement these types of parking spots at newer locations as EV ownership continues to expand to those who do more than simply drive their cars.
There are plenty of Superchargers in the New York City metro, but they are mostly located in boroughs outside of Manhattan. There are five Superchargers in various neighborhoods of Manhattan, but there are limited plugs; usually only four per location. There are plenty of Destination Chargers in the Big Apple, though.
Queens, the Bronx, and Brooklyn have become popular locations for companies to build out charging infrastructure for those who live in the highly populated boroughs. There is simply much more real estate to build effective EV charging stations.
The Supercharger will be located in Maspeth, Queens, at 48-26 54th Road. Maspeth has I-495 running through it, so this will be a great location for Tesla owners to hop off the highway on their way to Long Island or to Manhattan to charge up before continuing their journey.
Tesla has done a really great job of expanding its charging footprint throughout the past several years, especially by building large-scale projects that cater to areas that have a high volume of traffic and are main routes of travel to major areas. Tesla is making an effort to make charging less stressful and more widely available in these concentrated regions.
Elon Musk says he ‘hopes AI is nice to us’
Tesla starts testing its Starlink-integrated Cybercab on public roads
