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

(Credit: Tesla)

Published

4 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.

The Teslarati team would appreciate hearing from you. If you have any tips, contact me at maria@teslarati.com or via Twitter @Writer_01001101.

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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 Full Self-Driving v14.3.5 Early Impressions: new features and early performance

Published

3 hours ago

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July 14, 2026

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Credit: TESLARATI

Tesla rolled out Full Self-Driving (Supervised) v14.3.5 yesterday, and about fifty miles of driving on the new version has given me enough time to highlight what seems to be strong about the release and what is not.

Additionally, Tesla has added a few new features with this specific update, which we’ll highlight as well.

Tesla Full Self-Driving v14.3.5 Performance

The new update is business as usual. Things seem to be running completely normal and necessary, but there are a few things that we’ve seemed to pick up on based on our own experience with v14.3.5, as well as what other users are seeing.

Initially, it seems to be more aware of its surroundings, making moves that are incredibly courteous to other drives and operating just a tad more reserved than what the suite might have done previously.

We had two instances where it showed this, the first being FSD needing to pass a Flagger Force vehicle that was placing down signage for the day. Their work truck was right at the front corner of a right-hand turn; typically where most cars travel when they take that turn.

FSD v14.3.5 recognized this, slowed down, and took the turn wide with no issues:

Additionally, v14.3.5 backed up for a semi truck that was making a wide turn onto a road my car was on. This is not new, but it seemed to be backing up for courtesy; it didn’t seem completely necessary, but it might have put some peace of mind in the truck driver’s head:

X user Mike P, also a Pennsylvania native like myself, shared three clips of his Tesla running v14.3.5 performing similar maneuvers. He said:

“FSD turns right into a small alley that only fits one car at a time, sees oncoming car, reverses out of alley to make space, realizes oncoming car is actually parking, re-enters alley.”
Check it out here:

It seems like Speed Profiles are still in need of some tweaking; I am adjusting what Speed Profile I’m in frequently, constantly changing it to get it to travel at the correct speed. This was an issue for me on v14.3.4. It seems like they’re just a little inconsistent.

Terrible Parking

Parking attempts on v14.3.5 were not good. There are quite a few people who have said this:

David Moss, the Tesla owner who has taken multiple coast-to-coast drives without any interventions, also has had some issues with parking early on with v14.3.5:

New Features

Tesla has added the ability to open Camera Preview at any time. Previously, it was only available in Park. Here’s what that feature looks like in action:

Check back later this week for a longer review of what we’ve noticed on Full Self-Driving v14.3.5.

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Lifestyle

Tesla makes the cut on California’s newest EV Rebate program

California just signed a $270 million EV rebate into law and it starts this summer.

Published

8 hours ago

on

July 13, 2026

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tesla fremont

California Governor Gavin Newsom signed SB 168 into law on Monday, July 13, 2026, creating a $270 million EV rebate program that delivers money directly at the dealership rather than as a tax credit applied months later. The program, called MyFirstEV, is funded equally by California’s state budget and participating automakers, with each contributing $135.5 million to make the math work.

The timing is directly tied to the loss of federal support when the $7,500 federal EV tax credit ended, removing the most significant consumer incentive that had driven EV adoption in the U.S. California, which accounts for roughly one-third of all EVs sold nationally, moved to fill that gap with a state-level replacement.

The rebate structure is straightforward. First-time EV buyers can receive $3,500 off any new battery-electric vehicle with an MSRP up to $50,000. Used EVs priced at $25,000 or below qualify for a $1,750 rebate. The credit is applied at the point of sale, which removes the friction of the old federal system where buyers had to wait for tax season to see the benefit. The program goes live later this summer, with the California Air Resources Board expected to release full participation details next month.

California hits Tesla Cybercab and Robotaxi driverless cars with new law

For Tesla buyers, the implications are mixed. The Tesla Model 3 RWD at $42,490 and the Model 3 Long Range at $47,490 both fall under the $50,000 cap and would qualify for the full $3,500 rebate for first-time buyers. The Model Y, which starts at $44,990 after Tesla’s recent price adjustment, also qualifies. The Model X, Model S, and Cybertruck all exceed the cap and receive no benefit. As Teslarati has reported, the program also includes a carve-out exempting California-based automakers like Rivian and Lucid from the price cap entirely, a provision that puts Tesla at a disadvantage since it relocated its headquarters to Texas in 2021.

Other qualifying vehicles include the Chevrolet Equinox EV, Ford Mustang Mach-E, Hyundai Ioniq 5, Kia EV6, and Volkswagen ID.4.

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News

Tesla Semi enters new Pilot Program with interesting challenge

Published

20 hours ago

on

July 13, 2026

By

Credit: PTI

The Tesla Semi is entering a new Pilot Program with Paper Transport, LLC (PTI), a Wisconsin-based transportation provider. The company will test the Semi’s Long Range configuration through “dedicated operations within the Chicago market.”

Chicago presents an interesting challenge for the Semi, as it will be a colder-weather climate that will test the Semi’s ability to operate in lower temperatures and in potentially large accumulations of snow. This is something Tesla has been testing with the Semi in Alaska and even in Northern California during the colder months, but Chicago will present a truly tough midwestern winter.

Tesla Semi spotted on journey home after winter performance testing

PTI says it is using the Semi to evaluate its strategy of reducing transportation emissions while maintaining performance, reliability, and cost efficiency. These are major arguments for the Semi being introduced into new fleets.

CEO of PTI Tyler Ellison said:

“PTI has been a leader in sustainable transportation solutions for over 15 years. We take a consultative approach to helping customers identify and implement the right transportation solution for their network. Our partnership with Tesla expands our portfolio alongside renewable natural gas and intermodal, giving customers more ways to reduce Scope 3 emissions without compromising service or economics.”

PTI is far from the first company to adopt the Semi within a fleet, as Tesla entered strategic agreements with PepsiCo. and its subsidiary Frito-Lay for a Pilot Program that extended throughout the California region.

Tesla has let companies like those utilize the Semi to determine whether it would be suitable for their operations. Additionally, Tesla gets valuable information regarding the Semi’s performance, knowing what to improve and what is ideal for companies that will utilize the all-electric truck for regional and nationwide logistics.

PTI plans to utilize the Long Range configuration, which is priced at $290,000 and features a range of approximately 500 miles, a three-motor powertrain, up to 800 kW of drive power, and consumption of just 1.7 kWh per mile.

Tesla Semi pricing revealed after company uncovers trim levels

VP of Maintenance at PTI, Bryan Ellen, added:

“We are excited to partner with Tesla, leveraging their ever-evolving technology. We are bullish in our estimation of the parallels available between our dedicated model and the efficiency of their fully electric Class 8 tractor. We anticipate a growing synergy between our businesses as we work to facilitate this sustainable solution for our customers.”

PTI has logged more than 87 million miles using sources like compressed and renewable gas, but now is looking to take it a step further with fully electric operations.

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