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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.
News
BREAKING: Tesla launches public Robotaxi rides in Austin with no Safety Monitor
Tesla has officially launched public Robotaxi rides in Austin, Texas, without a Safety Monitor in the vehicle, marking the first time the company has removed anyone from the vehicle other than the rider.
The Safety Monitor has been present in Tesla Robotaxis in Austin since its launch last June, maintaining safety for passengers and other vehicles, and was placed in the passenger’s seat.
Tesla planned to remove the Safety Monitor at the end of 2025, but it was not quite ready to do so. Now, in January, riders are officially reporting that they are able to hail a ride from a Model Y Robotaxi without anyone in the vehicle:
I am in a robotaxi without safety monitor pic.twitter.com/fzHu385oIb
— TSLA99T (@Tsla99T) January 22, 2026
Tesla started testing this internally late last year and had several employees show that they were riding in the vehicle without anyone else there to intervene in case of an emergency.
Tesla has now expanded that program to the public. It is not active in the entire fleet, but there are a “few unsupervised vehicles mixed in with the broader robotaxi fleet with safety monitors,” Ashok Elluswamy said:
Robotaxi rides without any safety monitors are now publicly available in Austin.
Starting with a few unsupervised vehicles mixed in with the broader robotaxi fleet with safety monitors, and the ratio will increase over time. https://t.co/ShMpZjefwB
— Ashok Elluswamy (@aelluswamy) January 22, 2026
Tesla Robotaxi goes driverless as Musk confirms Safety Monitor removal testing
The Robotaxi program also operates in the California Bay Area, where the fleet is much larger, but Safety Monitors are placed in the driver’s seat and utilize Full Self-Driving, so it is essentially the same as an Uber driver using a Tesla with FSD.
In Austin, the removal of Safety Monitors marks a substantial achievement for Tesla moving forward. Now that it has enough confidence to remove Safety Monitors from Robotaxis altogether, there are nearly unlimited options for the company in terms of expansion.
While it is hoping to launch the ride-hailing service in more cities across the U.S. this year, this is a much larger development than expansion, at least for now, as it is the first time it is performing driverless rides in Robotaxi anywhere in the world for the public to enjoy.
Investor's Corner
Tesla Earnings Call: Top 5 questions investors are asking
Tesla has scheduled its Earnings Call for Q4 and Full Year 2025 for next Wednesday, January 28, at 5:30 p.m. EST, and investors are already preparing to get some answers from executives regarding a wide variety of topics.
The company accepts several questions from retail investors through the platform Say, which then allows shareholders to vote on the best questions.
Tesla does not answer anything regarding future product releases, but they are willing to shed light on current timelines, progress of certain projects, and other plans.
There are five questions that range over a variety of topics, including SpaceX, Full Self-Driving, Robotaxi, and Optimus, which are currently in the lead to be asked and potentially answered by Elon Musk and other Tesla executives:
- You once said: Loyalty deserves loyalty. Will long-term Tesla shareholders still be prioritized if SpaceX does an IPO?
- Our Take – With a lot of speculation regarding an incoming SpaceX IPO, Tesla investors, especially long-term ones, should be able to benefit from an early opportunity to purchase shares. This has been discussed endlessly over the past year, and we must be getting close to it.
- When is FSD going to be 100% unsupervised?
- Our Take – Musk said today that this is essentially a solved problem, and it could be available in the U.S. by the end of this year.
- What is the current bottleneck to increase Robotaxi deployment & personal use unsupervised FSD? The safety/performance of the most recent models or people to monitor robots, robotaxis, in-car, or remotely? Or something else?
- Our Take – The bottleneck seems to be based on data, which Musk said Tesla needs 10 billion miles of data to achieve unsupervised FSD. Once that happens, regulatory issues will be what hold things up from moving forward.
- Regarding Optimus, could you share the current number of units deployed in Tesla factories and actively performing production tasks? What specific roles or operations are they handling, and how has their integration impacted factory efficiency or output?
- Our Take – Optimus is going to have a larger role in factories moving forward, and later this year, they will have larger responsibilities.
- Can you please tie purchased FSD to our owner accounts vs. locked to the car? This will help us enjoy it in any Tesla we drive/buy and reward us for hanging in so long, some of us since 2017.
- Our Take – This is a good one and should get us some additional information on the FSD transfer plans and Subscription-only model that Tesla will adopt soon.
Tesla will have its Earnings Call on Wednesday, January 28.
Elon Musk
Elon Musk shares incredible detail about Tesla Cybercab efficiency
Elon Musk shared an incredible detail about Tesla Cybercab’s potential efficiency, as the company has hinted in the past that it could be one of the most affordable vehicles to operate from a per-mile basis.
ARK Invest released a report recently that shed some light on the potential incremental cost per mile of various Robotaxis that will be available on the market in the coming years.
The Cybercab, which is detailed for the year 2030, has an exceptionally low cost of operation, which is something Tesla revealed when it unveiled the vehicle a year and a half ago at the “We, Robot” event in Los Angeles.
Musk said on numerous occasions that Tesla plans to hit the $0.20 cents per mile mark with the Cybercab, describing a “clear path” to achieving that figure and emphasizing it is the “full considered” cost, which would include energy, maintenance, cleaning, depreciation, and insurance.
Probably true
— Elon Musk (@elonmusk) January 22, 2026
ARK’s report showed that the Cybercab would be roughly half the cost of the Waymo 6th Gen Robotaxi in 2030, as that would come in at around $0.40 per mile all in. Cybercab, at scale, would be at $0.20.

Credit: ARK Invest
This would be a dramatic decrease in the cost of operation for Tesla, and the savings would then be passed on to customers who choose to utilize the ride-sharing service for their own transportation needs.
The U.S. average cost of new vehicle ownership is about $0.77 per mile, according to AAA. Meanwhile, Uber and Lyft rideshares often cost between $1 and $4 per mile, while Waymo can cost between $0.60 and $1 or more per mile, according to some estimates.
Tesla’s engineering has been the true driver of these cost efficiencies, and its focus on creating a vehicle that is as cost-effective to operate as possible is truly going to pay off as the vehicle begins to scale. Tesla wants to get the Cybercab to about 5.5-6 miles per kWh, which has been discussed with prototypes.
Additionally, fewer parts due to the umboxed manufacturing process, a lower initial cost, and eliminating the need to pay humans for their labor would also contribute to a cheaper operational cost overall. While aspirational, all of the ingredients for this to be a real goal are there.
It may take some time as Tesla needs to hammer the manufacturing processes, and Musk has said there will be growing pains early. This week, he said regarding the early production efforts:
“…initial production is always very slow and follows an S-curve. The speed of production ramp is inversely proportionate to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow, but eventually end up being insanely fast.”