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Tesla FSD Beta 10.69 release notes highlight better left turns, smoother driving

(Credit: Tesla)

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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.

Maria--aka "M"-- is an experienced writer and book editor. She's written about several topics including health, tech, and politics. As a book editor, she's worked with authors who write Sci-Fi, Romance, and Dark Fantasy. M loves hearing from TESLARATI readers. If you have any tips or article ideas, contact her at maria@teslarati.com or via X, @Writer_01001101.

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Tesla Assisted Driving expands to tenth EU country

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Tesla has expanded its Tesla Assisted Driving (TAD) suite to its tenth European country, as Latvia joined a growing list of European Union member states authorizing the company’s advanced driver assistance technology.

Tesla Europe announced this morning that the system is now approved in Latvia, with a software rollout to begin soon.

The approval and rollout follow recognition of a provisional European type approval originally granted by the Netherlands’ RDW authority on April 10.

Latvia’s Road Traffic Safety Directorate (CSDD) evaluated the Dutch documentation, coordinated with the other national authorities, and reviewed updates from Tesla. These included tightened limits on speed offsets, automatic resets of any manual speed adjustments after each trip, and stronger driver monitoring.

In Europe, the feature is marketed as Tesla Assisted Driving after a recent rule change that labeled the name “Full Self-Driving” as misleading.

Tesla starts showing how FSD will change lives in Europe

The system remains an SAE Level 2 driver assistance tool, not unsupervised autonomy. It can control speed, steering, braking, lane changes, and intersections while following a navigation route. Drivers need not keep their hands on the wheel at all times, yet they must continuously monitor the road and be prepared to intervene immediately.

The sequence of EU recognitions began with the Netherlands in April and continued through Lithuania, Estonia, Denmark, Belgium, Slovenia, Czechia, Croatia, and Slovakia. Most have relied on mutual recognition of the single Dutch provisional approval rather than independent national testing.

These national clearances do not yet constitute bloc-wide authorization. An EU-wide decision requires a qualified majority in the Technical Committee on Motor Vehicles: at least 15 of 27 member states representing 65 percent of the EU population. Larger markets, like Germany, France, and Italy, remain under review, and a vote is expected this December.

However, for Latvian Tesla owners with compatible vehicles and software, the imminent rollout expands access to the supervised system while underscoring that attentive human oversight remains essential.

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Tesla expands Robotaxi geofence in Houston with an ode to SpaceX merger

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Tesla expanded its Robotaxi geofence in Houston, Texas, today, enlarging the service area and drawing a new rocket-shaped boundary across the city’s northwestern and central parts.

Although CEO Elon Musk said it was just a coincidence, the expansion seems to be an ode to SpaceX and its potential merger with Tesla.

The official Tesla Robotaxi account announced the change with the lines “Houston, we have liftoff” and “Bigger service area now live,” pairing them with a map overlay.

The prior white geofence, roughly 25 square miles covering Jersey Village, Harvest Bend, and the Willowbrook corridor along U.S. 290 and State Highway 249, now sits inside a larger blue zone that stretches southeast toward Bellair and closer to central Houston.

Houston’s Robotaxi service launched on April 18, 2026, the same day as Dallas, as Tesla’s first unsupervised public expansion beyond Austin. From the start, the vehicles were Model Ys running without anyone in the front seats, booked through the dedicated Tesla Robotaxi app and open to the public inside the geofence.

Trackers measured the initial zone at about 25 square miles; by late September it had not grown substantially. Community and registry data indicated a modest local fleet of unsupervised Model Ys, while dozens to nearly 200 Cybercabs, the purpose-built, steering-wheel-free two-seaters, have been spotted staging and testing in Houston lots, though public Cybercab rides remain limited to Austin for now.

Across the broader Robotaxi program, Tesla began public Robotaxi rides in Austin in June 2025, as a small geofence and vehicles with safety monitors became available for a ride-hailing service. Later on, Tesla removed the monitors and steadily expanded the service area until it covered most of the Austin metro area.

Unsupervised Robotaxi services now run in six cities: Austin, Dallas, Houston, Miami, Orlando, and Tampa. Cumulative driverless miles reported by the company have reached several million. The Cybercab entered public service in Austin in early September 2026 and has scaled rapidly there. The company has also said that Dallas is next for Cybercab rides in November, with further cities and 24/7 hours targeted by year-end, plus fleet-operator sales of the vehicle.

Waymo also operates a fully driverless service in Houston, which has been open to the public since August of this year.

The Houston expansion continues Tesla’s pattern of starting with tightly controlled validation zones before widening coverage as real-world data accumulates, while the rocket motif on the map underscores the company’s habit of injecting visual flair into its autonomy rollout.

Riders inside the new boundary can request unsupervised Model Y trips in the Robotaxi app; exact operating hours and further Cybercab availability remain subject to app updates and company statements.

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Tesla Cybercabs take over Austin’s F1 weekend

Tesla Cybercabs swarmed Austin’s F1 Grand Prix as sightings hit a record during race weekend.

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Tesla’s driverless Cybercabs used the Formula 1 United States Grand Prix in Austin as their biggest public stress test yet. On Sunday afternoon, Tesla head of AI software Ashok Elluswamy quoted a photo from X user Ben Steindl showing a long line of gold Cybercabs and Model Y Robotaxis stacked up on a road near Circuit of the Americas. “Robotaxis as far as the eye can see!” Elluswamy wrote. Steindl’s original post was more matter of fact: “@Tesla @robotaxi is busy for the Austin F1.”

The fleet also handled the trip home. The race started at 3 p.m. Central, and once it ended, Cybercab Spotter, an account that logs Cybercab sightings around Austin, posted that “People are requesting Cybercabs to get home.”

Tesla set this up days in advance without announcing it. Late on October 22, rider David Moss posted Robotaxi app screenshots showing the Austin service area wrapping around the track toward Elroy and FM 812, with ride requests open until 1 a.m. A banner in the app read “Last call is 1am · Limited to nearby dropoffs.” Austin rides ended at 11 p.m. as recently as early October, as Teslarati reported, before moving to midnight on October 17.

Not every rider got dropped at the gate. Early app searches showed spots like COTA Karting as “Service blocked,” and one rider who took a Cybercab to the track Friday said the rideshare area was less than a 20 minute walk from the front gate.

The weekend also produced a new activity record. Cybercab Spotter counted 1,250 Cybercab sightings in Austin on Saturday, qualifying day. The previous high was 1,072 on October 16. Sightings count the same car more than once, so they measure how busy the fleet is rather than how big it is. For size, the Texas automated vehicle registry listed 463 Cybercabs at midday Sunday, up from the 300+ Tesla claimed on October 10, when Elluswamy also said Dallas is next for Cybercab.

The track was out of reach for Tesla’s main rival. Waymo runs in Austin only through Uber, and its service area stops well short of COTA. Waymo did not announce any race day service.

Tesla has not said how many rides it gave to and from the circuit, or whether the expanded zone and 1 a.m. cutoff will stay after race weekend. On the third quarter earnings call, Elluswamy said 2 a.m. service is coming “pretty soon” and that Tesla plans to run Robotaxi around the clock before the end of the year. The company is also aiming to hand the first Cybercabs to customers before 2026 ends, which would put cars like these outside Tesla’s own fleet for the first time.

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