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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 is beginning to roll out Full Self-Driving Supervised v14.3.9 with a new active safety layer that can take control even when the driver is operating the car manually.
Tesla AI said the software can activate FSD on the driver’s behalf when an imminent collision is detected and Automatic Emergency Braking may not be enough. It may also engage if the system detects heavy distraction or an accidental FSD disengagement.
FSD Supervised v14.3.9 starting to roll out shortly
This release includes a new active safety feature set: FSD Supervised can now activate on your behalf when an imminent collision is detected and Automatic Emergency Braking (AEB) may not be enough.
It may also engage if we…
— Tesla AI (@Tesla_AI) September 4, 2026
The capability is essentially Automatic Collision Evasion. However, unlike conventional AEB, which mainly applies the brakes in a straight line, this feature can use steering, braking, and acceleration together if the car calculates that stopping alone will not prevent impact and a safer path exists. The system may change lanes or move toward a shoulder when conditions allow, then continue driving after the immediate threat is handled rather than simply coming to a stop.
The intervention is meant as a last-resort safety net, not a replacement for attentive driving.
Tesla Full Self-Driving v14.3.7 early review: FSD saved me from an accident
Tesla’s own description still frames FSD as supervised assistance. Secondary reports on internal release notes say the feature can fire while the car is being driven manually if cabin-camera monitoring suggests the driver is not sufficiently attentive, such as reaching toward the back seat, or if FSD appears to have been turned off unintentionally.
After the emergency maneuver, the car is expected to alert the driver and request a return to manual control.
The safety case is straightforward. Many collisions happen in the last second because a driver is looking away, fumbles a control, or faces an obstacle that braking cannot fully solve. A system that can both recognize that AEB is insufficient and execute a coordinated evasive path can reduce those remaining high-severity events.
Re-engaging after accidental disengagement also addresses a practical failure mode: a small steering nudge that drops FSD at the worst moment. The advantage is a background safety net that uses the same vision stack already running in v14, instead of leaving the car solely to emergency braking once the driver is no longer in command.
The feature still depends on FSD being enabled and, according to reports, an active FSD purchase or subscription. It does not make the vehicle unsupervised. Drivers remain responsible, and Tesla has not published how often the system is expected to intervene or how it will handle false positives.
If the rollout is conservative and the false-alarm rate stays low, the update is a meaningful step: FSD is no longer only a feature the driver turns on. In the rare moments when disaster is already forming, it can step in.
Tesla launched the all-electric, steering wheel-less, and pedal-less Cybercab last night at a quiet and small event in downtown Austin, Texas.
The launch, which marked the beginning of unsupervised ride-hailing for Tesla’s Robotaxi platform with Cybercab, has already caught the attention of the National Highway Traffic Safety Administration (NHTSA) who has more questions.
NHTSA opened an Audit Query (AQ) into the Cybercab’s Federal Motor Vehicle Safety Standards (FMVSS) certification that Tesla gave the vehicle. Manufacturers self-certify vehicles much of the time to avoid excessive regulatory delays.
Tesla Cybercab interior, note the lack of steering wheel and pedals. (Credit: @niccruzpatane/X< /a>)
However, the agency needs more information; it said in a summary:
“On September 3, 2026, Tesla began commercial deployment with a small number of its Cybercab vehicles in Austin, Texas. Tesla notified the Agency that it certified those Cybercab vehicles as compliant with all applicable Federal Motor Vehicle Safety Standards (FMVSS). Tesla also notified the Agency that it plans to gradually expand commercial deployment of the Cybercab to include additional vehicles and locations.”
It also went on to state that the Cybercab lacks traditional automotive controls, which is a groundbreaking move. The process is entirely new to the NHTSA, which gives the agency some leverage to put Tesla’s launch under a microscope:
“The vehicles lack permanently attached, conventional manual controls, such as a brake pedal, gas pedal, steering wheel, and mirrors. NHTSA is opening this AQ to examine the process and technical data on which Tesla relied when certifying the Cybercab and related issues. Among other things, NHTSA will consider the extent to which Tesla’s certification depended on determinations that certain FMVSS are inapplicable to the Cybercab.”
Tesla has added 45 Cybercab units to its fleet of Robotaxi-enabled cars in Austin, according to public documents the company submitted to the State of Texas over the past week. Enabling this level of self-driving is something Tesla has worked toward for many years, and now that it is finally here, it seems more than reasonable that regulatory agencies will have some questions.
Many outlets might try to frame this as a negative, but it is truly an agency looking to gain more information about groundbreaking tech that Tesla has been developing for years.
In an effort to keep riders, pedestrians, and property safe, any and all data accumulated from these first days, weeks, and months of rides will likely be shared with the NHTSA to enable broader rollout strategies across the United States and more in the future.
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Tesla Cybercab is coming to Asia this month as US service officially begins
Tesla Asia says Cybercab will be on display in Hong Kong, Tokyo, Beijing and Shanghai this month.
Tesla’s Cybercab is heading to Asia. The official Tesla Asia account posted on X Thursday, inviting Cybercab fans to “Come experience the future of autonomy in Hong Kong, Tokyo, Beijing & Shanghai.” The post went up within hours of Tesla’s own Cybercab milestone in Texas, where the company said Thursday it had begun offering rides in across Austin.
Exact dates and venues for the Asia tour haven’t been released yet, though Tesla Hong Kong replied to the announcement with “Cybercab will be on display in Hong Kong soon,” while Tesla Japan’s response pointed fans to a sign up page for updates. Neither post mentions test rides or a service area, and nothing so far suggests Tesla is launching Robotaxi operations in any of the four cities. Based on how Tesla has run past Cybercab tours, in Europe in late 2024 and at US shopping centers that same December, the Asia stops are almost certainly static displays at Tesla stores or public venues as a means to stimulate buzz for its future driverless ride-hailing service in the big cities.
Cybercab will be on display in Asia this month!
Come experience the future of autonomy in Hong Kong, Tokyo, Beijing & Shanghai. pic.twitter.com/wGmmEastfX— Tesla Asia (@Tesla_Asia) September 4, 2026
The timing lines up with Tesla’s only prior Cybercab appearance in the region, a booth at the China International Import Expo in Shanghai last November, which Teslarati covered at the time. At that event, Tesla’s regional general manager for Shanghai framed the car as evidence of the company’s broader mission, a message Tesla has since formalized in its Master Plan Part IV, which states that “autonomous vehicles have the capacity to dramatically improve the affordability, availability and safety of transportation while reducing pollution, particularly in our increasingly dense global cities.” The same document is where Tesla lays out its “sustainable abundance” framing for Cybercab and Optimus alike, describing the two as the hardware behind an AI driven push to cut the cost of transportation and labor at scale.
Whether Cybercab actually operates as a robotaxi anywhere in Asia remains an open question, considering China has already pushed an autonomous ride-hailing market that’s run on homegrown players like Baidu’s Apollo Go and Pony AI. For now, the four city tour reads as a marketing push timed to Austin’s momentum.
Tesla Full Self-Driving will now overtake manual driving to avoid disaster
Tesla Cybercab launch catches NHTSA’s attention who wants to know more
