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

(Credit: Tesla)

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3 years ago

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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.

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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 gamifies Supercharging with new ‘Charging Passport’

It will also include things like badges for special charging spots, among other metrics that will show all of the different places people have traveled to plug in for range.

Published

3 hours ago

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December 8, 2025

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Credit: MarcoRP | X

Tesla is gamifying its Supercharging experience by offering a new “Charging Passport,” hoping to add a new layer to the ownership experience.

While it is not part of the Holiday Update, it is rolling out around the same time and offers a handful of cool new features.

Tesla’s Charging Passport will be available within the smartphone app and will give a yearly summary of your charging experience, helping encapsulate your travel for that year.

It will also include things like badges for special charging spots, among other metrics that will show all of the different places people have traveled to plug in for range.

Tesla will include the following metrics within the new Charging Passport option within the Tesla app:

  • Charging badges: Iconic charging badges for visiting places like the Tesla Diner, Oasis Supercharger, etc., Explorer Badge, and more
  • Total Unique Superchargers Visited
  • Total Charging Sessions
  • Total Miles Added during Charging Sessions
  • Top Charging Day
  • Longest Trip
  • Favorite Charging Locations

This will give people a unique way to see their travels throughout the year, and although it is not necessarily something that is needed or adds any genuine value, it is something that many owners will like to look back on. After all, things like Spotify Wrapped and Apple Music Replay have been a great way for people to see what music they listened to throughout the year.

This is essentially Tesla’s version of that.

With a handful of unique Superchargers already active, Tesla is also building some new ones, like a UFO-inspired location in New Mexico, near Roswell.

Tesla is building a new UFO-inspired Supercharger in the heart of Alien country

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Tesla launches its coolest gift idea ever just a few weeks after it was announced

“Gift one month of Full Self-Driving (Supervised), which allows the vehicle to drive itself almost anywhere with minimal intervention.”

Published

5 hours ago

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December 8, 2025

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Credit: Tesla

Tesla has launched its coolest gift idea ever, just a few weeks after it was announced.

Tesla is now giving owners the opportunity to gift Full Self-Driving for one month to friends or family through a new gifting program that was suggested to the company last month.

The program will enable people to send a fellow Tesla owner one month of the company’s semi-autonomous driving software, helping them to experience the Full Self-Driving suite and potentially help Tesla gain them as a subscriber of the program, or even an outright purchase.

Tesla has officially launched the program on its Shop. Sending one month of Full Self-Driving costs $112:

“Gift one month of Full Self-Driving (Supervised), which allows the vehicle to drive itself almost anywhere with minimal intervention. All sales are final. Can only be purchased and redeemed in the U.S. This gift card is valued at $112.00 and is intended to cover the price of one month of FSD (Supervised), including up to 13% sales tax. It is not guaranteed to cover the full monthly price if pricing or tax rates change. This gift card can be stored in Tesla Wallet and redeemed toward FSD (Supervised) or any other Tesla product or service that accepts gift card payments.”

Tesla has done a great job of expanding Full Self-Driving access over the past few years, especially by offering things like the Subscription program, free trials through referrals, and now this gift card program.

Gifting Full Self-Driving is another iteration of Tesla’s “butts in seats” strategy, which is its belief that it can flip consumers to its vehicles and products by simply letting people experience them.

There is also a reason behind pushing Full Self-Driving so hard, and it has to do with CEO Elon Musk’s compensation package. One tranche requires Musk to achieve a certain number of active paid Full Self-Driving subscriptions.

More people who try the suite are likely to pay for it over the long term.

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Tesla expands Robotaxi app access once again, this time on a global scale

Tesla said recently it plans to launch Robotaxi in Miami, Houston, Las Vegas, Phoenix, and Dallas.

Published

6 hours ago

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December 8, 2025

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Credit: Grok

Tesla has expanded Robotaxi app access once again, but this time, it’s on a much broader scale as the company is offering the opportunity for those outside of North America to download the app.

Tesla Robotaxi is the company’s early-stage ride-hailing platform that is active in Texas, California, and Arizona, with more expansion within the United States planned for the near future.

Tesla said recently it plans to launch Robotaxi in Miami, Houston, Las Vegas, Phoenix, and Dallas.

The platform has massive potential, and Tesla is leaning on it to be a major contributor to even more disruption in the passenger transportation industry. So far, it has driven over 550,000 miles in total, with the vast majority of this coming from the Bay Area and Austin.

First Look at Tesla’s Robotaxi App: features, design, and more

However, Tesla is focusing primarily on rapid expansion, but most of this is reliant on the company’s ability to gain regulatory permission to operate the platform in various regions. The expansion plans go well outside of the U.S., as the company expanded the ability to download the app to more regions this past weekend.

So far, these are the areas it is available to download in:

  • Japan
  • Thailand
  • Hong Kong
  • South Korea
  • Australia
  • Taiwan
  • Macau
  • New Zealand
  • Mexico
  • U.S.
  • Canada

Right now, while Tesla is focusing primarily on expansion, it is also working on other goals that have to do with making it more widely available to customers who want to grab a ride from a driverless vehicle.

One of the biggest goals it has is to eliminate safety monitors from its vehicles, which it currently utilizes in Austin in the passenger’s seat and in the driver’s seat in the Bay Area.

A few weeks ago, Tesla started implementing a new in-cabin data-sharing system, which will help support teams assist riders without anyone in the front of the car.

Tesla takes a step towards removal of Robotaxi service’s safety drivers

As Robotaxi expands into more regions, Tesla stands to gain tremendously through the deployment of the Full Self-Driving suite for personal cars, as well as driverless Robotaxis for those who are just hailing rides.

Things have gone well for Tesla in the early stages of the Robotaxi program, but expansion will truly be the test of how things operate going forward. Navigating local traffic laws and gaining approval from a regulatory standpoint will be the biggest hurdle to jump.

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