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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.
A close-up image of a Cybercab engineering vehicle in Peabody, Massachusetts, reveals a compact triangular side repeater camera housing equipped with an integrated washer mechanism.
This seemingly small hardware addition could prove to be one of the most critical components for achieving reliable, unsupervised Full Self-Driving (FSD) — not just for the dedicated Robotaxi but potentially for existing AI4-equipped vehicles as well.
The washer system’s importance cannot be overstated in Tesla’s vision-only autonomy approach. Cameras are the sole sensory input for the neural networks powering FSD, constantly interpreting the environment for safe navigation. In real-world conditions, however, lenses quickly accumulate rain, snow, mud, dust, or road spray.
Many of us Tesla owners, especially those who deal with any sort of winter weather at all, know the all-too-common alert that pops up when cameras are obstructed:
Even brief obstructions can drop perception confidence, trigger safety disengagements, or force the vehicle to pull over, although these are relatively rare. Instead, most of the time, the camera will need a wipe from the owner next time they stop the car.
But unlike human drivers who can manually clear their view, a Robotaxi operating 24/7 without a steering wheel or mirrors must maintain pristine vision autonomously. The Cybercab’s side repeater washer delivers targeted cleaning bursts precisely where needed for merging, lane changes, and blind-spot monitoring — functions that demand uninterrupted visibility from the external cameras:
And this is how the side camera and washer look like on a Cybercab. This is from an Engineering vehicle in Peabody MA. pic.twitter.com/Re8VknpmLM
— Tobias Goebel (Unsupervised) (@tpgoebel) June 17, 2026
This hardware directly tackles a known pain point in current FSD deployments. Owners frequently report camera-related alerts during inclement weather, which is understandable, but needs to be solved for a true autonomous experience.
For a production Robotaxi fleet aiming for high utilization and minimal downtime, robust washer systems represent a foundational reliability upgrade; essentially, they’re a must-have. Early sightings suggest the design may extend to rear cameras as well, creating a comprehensive cleaning architecture that keeps the entire vision suite operational in harsh environments.
Without it, even the most advanced neural nets struggle when their “eyes” are compromised.
What Does This Mean for AI4 Cars?
This Cybercab detail raises timely questions for AI4 cars already on the road. While Hardware 4 delivers superior compute and camera resolution compared to earlier versions, production models typically lack dedicated side and rear washers. Tesla has included them on Model Y robotaxis that it is using in the fleet:
Tesla Robotaxi has a highly-requested hardware feature not available on typical Model Ys
As Tesla refines unsupervised FSD for broader release, the gap in environmental resilience becomes evident. Software improvements can help mitigate issues, but they cannot fully replace physical cleaning in heavy rain or muddy conditions. Analysts and owners increasingly speculate that AI4 vehicles may eventually require similar washer retrofits — or a future AI4.5 variant — to match the Cybercab’s all-weather readiness and support the same level of autonomy.
As testing progresses, the Cybercab’s washer mechanism highlights Tesla’s pragmatic focus on real-world robustness. It may well become the hardware piece that determines how quickly and reliably FSD scales from prototypes to everyday vehicles.
Elon Musk
Elon Musk just upped his Tesla stake further fueling SpaceX merger conversation
Elon Musk just collected a $116 billion Tesla payday and the timing is eye-opening
Elon Musk quietly collected one of the largest single-transaction paydays in corporate history on Monday. A Form 4 filed with the SEC on June 17, 2026 disclosed that Musk exercised 303,960,630 Tesla stock options from his 2018 compensation package, with the transaction dated June 16. No shares were sold on the open market.
The numbers are straightforward but striking. Musk exercised the options at a split-adjusted strike price of $23.34, with Tesla closing at $404.66 that day, putting the spread at $381.32 per share and generating roughly $115.9 billion in paper gains in a single transaction. To cover the exercise cost, Tesla withheld 17,531,857 shares through a net share settlement, meaning Musk paid nothing out of pocket.
For perspective, in 2018, Elon Musk’s award was originally approved by Tesla shareholders on March 21, 2018, and structured entirely around performance milestones that many analysts at the time called unreachable. Every tranche eventually vested. The original grant covered 20,264,042 shares at $350.02, which after Tesla’s 5-for-1 split in 2020 and 3-for-1 split in 2022 adjusted to 303,960,630 shares at $23.34. A Delaware court rescinded the award in January 2024, ruling the board was conflicted. As Teslarati reported, Tesla shareholders voted to ratify the package anyway in June 2024 by a wide margin. The Delaware Supreme Court reversed the decision in December 2025, finding full cancellation too extreme, and Tesla’s board signed an Implementation Agreement on April 21, 2026 to formally deliver the shares.
The Tesla and SpaceX merger everyone is talking about is quietly building
The timing and structure of the Form 4 filing carries more weight than a routine stock option exercise typically would. Musk exercised his 2018 Tesla award on June 16, a week into SpaceX completing its IPO and trading publicly, and giving SpaceX a public market valuation and share currency for the first time in the company’s history. A stock-for-stock merger between two companies requires the acquiring entity to have tradeable shares it can offer to the target’s shareholders, and SpaceX now has exactly that. At the same time, Musk just increased his direct Tesla voting power to approximately 20%, giving him greater influence over any shareholder vote that a merger would require. The restricted shares he received cannot be sold until 2033, which removes any near-term incentive to cash out and instead positions this stake as long-term structural collateral in a deal. Additionally, Musk’s two companies are already deeply intertwined through shared semiconductor fabrication at their joint TERAFAB facility in Austin, cross-company supply chain transactions, and Tesla’s $2 billion investment in xAI prior to the SpaceX-xAI merger.
Wedbush analyst Dan Ives has publicly placed the odds of a Tesla and SpaceX combination at 80% to 90% by early 2027. The Implementation Agreement that made Monday’s exercise possible was signed on April 21, 2026, roughly two months before the SpaceX IPO closed. That sequencing, building Musk’s Tesla ownership to its highest point ever immediately before SpaceX gains the public currency needed to acquire it, is either an extraordinary coincidence or a carefully staged foundation for the largest corporate merger in history.
Tesla Full Self-Driving is going to be getting a major parking upgrade. That’s according to CEO Elon Musk, who detailed a crafty new feature that will improve parking preferences, removing a layer of human input.
Musk said that upcoming releases of Full Self-Driving will “remember your parking preferences.” It will go to the location you prefer, based on where you’ve parked in the past, instead of taking the first spot available, which is where the suite is currently.
The CEO went on to explain that destination parking is “by far” the biggest reason for intervention during FSD operation. We’d have to believe this is true; many takeovers in my Model Y, which runs the latest version of FSD as it is in the Early Access Program, are due to parking because it chooses a spot I do not want to be in.
Many times, as soon as I enter a parking lot, I take over and park manually. I prefer to park away from the entrance of wherever I am, away from cars. Too many lessons learned over the years from people with free-swinging doors.
Upcoming releases of FSD will remember your parking preferences, so that the car goes to the right location at your home, office, school drop off, etc.
Destination parking is by far the biggest reason people now intervene with FSD. Critical safety interventions are extremely…
— Elon Musk (@elonmusk) June 17, 2026
We’d imagine these new updates will also solve things like parking orientation. Let’s say when you arrive at work, you always park in the third spot in the third row, and you prefer to back in. It seems as if Musk is implying that your car will now do this, learning from takeovers and aiming to eliminate the need to manually park whenever possible.
This is a major upgrade because parking is a major shortcoming of FSD currently. We’ve requested things like manual input of parking preferences, choosing to park far away, first available, or away from cars, for example.
This is a big reason Parking Preferences with Supervised FSD will be so valuable.
If possible, parking a little further away and being distant from people like this is worth it. https://t.co/1YqQLgnfTz pic.twitter.com/3Ac71KQiQ3
— TESLARATI (@Teslarati) June 7, 2026
However, some have used the option of dropping a pin at the location you’d like to park at your destination. This has worked some of the time, but FSD will still choose to park in whatever it sees first.
Musk did not give a timetable for when the improvements would be released, but it is likely to come soon. Tesla has been releasing a new FSD version every few weeks, so we may not have to wait long to test it.
Tesla Cybercab has one important piece that AI4 cars might need for FSD
Elon Musk just upped his Tesla stake further fueling SpaceX merger conversation
