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Tesla FSD Beta V11.3 starts shipping to employees (Release Notes)

Credit: Drive in EV/Twitter

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The release notes for Tesla FSD Beta V11.3 have been shared online. Observers from the electric vehicle community suggest that Tesla Full Self-Driving Beta 11.3 is rolling out to the company’s employee FSD Beta testers, at least for now. 

The following are Tesla’s FSD Beta V11.3 release notes

  • Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks, and was setup to handle simple lane-specific maneuvers. FSD Beta’s multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
  • Added voice drive-notes. After an intervention, you can now send Tesla an anonymous voice message describing your experience to help improve Autopilot.
  • Expanded Automatic Emergency Braking (AEB) to handle vehicles that cross ego’s path. This includes cases where other vehicles run their red light or turn across ego’s path, stealing the right-of-way.
  • Replay of previous collisions of this type suggests that 49% of the events would be mitigated by the new behavior. This improvement is now active in both manual driving and autopilot operation.
  • Improved autopilot reaction time to red light runners and stop sign runners by 500ms, by increased reliance on object’s instantaneous kinematics along with trajectory estimates.
  • Added a long-range highway lanes network to enable earlier response to blocked lanes and high curvature.
  • Reduced goal pose prediction error for candidate trajectory neural network by 40% and reduced runtime by 3X. This was achieved by improving the dataset using heavier and more robust offline optimization, increasing the size of this improved dataset by 4X, and implementing a better architecture and feature space.
  • Improved occupancy network detections by oversampling on 180K challenging videos including rain reflections, road debris, and high curvature.
  • Improved recall for close-by cut-in cases by 20% by adding 40k autolabeled fleet clips of this scenario to the dataset. Also improved handling of cut-in cases by improved modeling of their motion into ego’s lane, leveraging the same for smoother lateral and longitudinal control for cut-in objects.
  • Added “lane guidance module and perceptual loss to the Road Edges and Lines network, improving the absolute recall of lines by 6% and the absolute recall of road edges by 7%.
  • Improved overall geometry and stability of lane predictions by updating the “lane guidance” module representation with information relevant to predicting crossing and oncoming lanes.
  • Improved handling through high speed and high curvature scenarios by offsetting towards inner lane lines. 
  • Improved lane changes, including: earlier detection and handling for simultaneous lane changes, better gap selection when approaching deadlines, better integration between speed-based and nav-based lane change decisions and more differentiation between the FSD driving profiles with respect to speed lane changes.
  • Improved longitudinal control response smoothness when following lead vehicles by better modeling the possible effect of lead vehicles’ brake lights on their future speed profiles.
  • Improved detection of rare objects by 18% and reduced the depth error to large trucks by 9%, primarily from migrating to more densely supervised autolabeled datasets.
  • Improved semantic detections for school busses by 12% and vehicles transitioning from stationary-to-driving by 15%. This was achieved by improving dataset label accuracy and increasing dataset size by 5%.
  • Improved decision making at crosswalks by leveraging neural network based ego trajectory estimation in place of approximated kinematic models.
  • Improved reliability and smoothness of merge control, by deprecating legacy merge region tasks in favor of merge topologies derived from vector lanes.
  • Unlocked longer fleet telemetry clips (by up to 26%) by balancing compressed IPC buffers and optimized write scheduling across twin SOCs.

Several longtime FSD Beta testers have pointed out some key improvements that would likely be very appreciated by users in V11.3. These include the systems’ improved handling through high speed and high curvature scenarios, as well as improvements to Automatic Emergency Braking (AEB). With the improvements in place, FSD Beta V11.3 would behave closer to a proper human driver. 

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Comments from longtime Tesla FSD Beta testers also suggest that V11.3 is still only being released for company employees for now. Considering Tesla’s past updates, it would not be surprising if the greater FSD Beta fleet gets the V11.3 update in the coming week or so. This is, of course, unless V11.3 ends up going the way of FSD Beta V11, which was released to employees in November but not to the greater fleet of FSD Beta testers. 

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 Diner becomes latest target of gloom and doom narrative

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tesla diner
Credit: Tesla

The Tesla Diner has been subject to many points of criticism since its launch in mid-2025, and skeptics and disbelievers claim the company’s latest novel concept is on its way down, but there’s a lot of evidence to state that is not the case.

The piece cites anecdotal evidence like empty parking lots, more staff than customers during a December visit, removed novelty items, like Optimus robot popcorn service and certain menu items, the departure of celebrity chef Eric Greenspan in November 2025, slow service, high prices, and a shift in recent Google/Yelp reviews toward disappointment.

The piece frames this as part of broader Tesla struggles, including sales figures and Elon Musk’s polarizing image, calling it a failed branding exercise rather than a sustainable restaurant.

This narrative is overstated and sensationalized, and is a good representation of coverage on Tesla by today’s media.

Novelty Fade is Normal, Not Failure

Any hyped launch, especially a unique Tesla-branded destination blending dining, Supercharging, and a drive-in theater, naturally sees initial crowds taper off after the “Instagram effect” wears down.

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Tesla makes major change at Supercharger Diner amid epic demand

This is common for experiential spots in Los Angeles, especially pop-up attractions or celebrity-backed venues. The article admits early success with massive lines and social media buzz, but treats the return to normal operations as “dying down.”

In reality, this stabilization is a healthy sign of transitioning from hype-driven traffic to steady patronage.

Actual Performance Metrics Contradict “Ghost Town” Claims

  • In Q4 2025, the Diner generated over $1 million in revenue, exceeding the average McDonald’s location
  • It sold over 30,000 burgers and 83,000 fries in that quarter alone. These figures indicate a strong ongoing business, especially for a single-location prototype focused on enhancing Supercharger experiences rather than competing as a mass-market chain

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Conflicting On-the-Ground Reports

While the article, and other similar pieces, describe a half-full parking lot and sparse customers during specific off-peak visits, other recent accounts push back:

  • A January 2026 X post noted 50 of 80 Supercharger stalls were busy at 11 a.m., calling it “the busiest diner in Hollywood by close to an order of magnitude

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  • Reddit discussions around the same time describe it as not empty when locals drive by regularly, with some calling the empty narrative “disingenuous anti-Tesla slop.”

Bottom Line

The Tesla Diner, admittedly, is not the nonstop circus it was at launch–that was never sustainable or intended. But, it’s far from “dying” or an “empty pit stop.”

It functions as a successful prototype: boosting Supercharger usage, generating solid revenue, and serving as a branded amenity in the high-traffic EV market of Los Angeles.

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Tesla stands to win big from potential adjustment to autonomous vehicle limitations

Enabling scale, innovation, and profitability in a sector that is growing quickly would benefit Tesla significantly, especially as it has established itself as a leader.

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Credit: Patrick Bean | X

Tesla stands to be a big winner from a potential easing of limitations on autonomous vehicle development, as the United States government could back off from the restrictions placed on companies developing self-driving car programs.

The U.S. House Energy and Commerce subcommittee will hold a hearing later this month that will aim to accelerate the deployment of autonomous vehicles. There are several key proposals that could impact the development of self-driving cars and potentially accelerate the deployment of this technology across the country.

These key proposals include raising the NHTSA’s exemption cap from 2,500 to 90,000 vehicles per year per automaker, preempting state-level regulations on autonomous vehicle systems, and mandating NHTSA guidelines for calibrating advanced driver assistance systems (ADAS).

Congress, to this point, has been divided on AV rules, with past bills like the 2017 House-passed measure stalling in the Senate. Recent pushes come from automakers urging the Trump administration to act faster amid competition from Chinese companies.

Companies like Tesla, who launched a Robotaxi service in Austin and the Bay Area last year, and Alphabet’s Waymo are highlighted as potential beneficiaries from lighter sanctions on AV development.

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The NHTSA recently pledged to adopt a quicker exemption review for autonomous vehicle companies, and supporters of self-driving tech argue this will boost U.S. innovation, while critics are concerned about safety and job risks.

How Tesla Could Benefit from the Proposed Legislation

Tesla, under CEO Elon Musk’s leadership, has positioned itself as a pioneer in autonomous driving technology with its Full Self-Driving software and ambitious Robotaxi plans, including the Cybercab, which was unveiled in late 2024.

The draft legislation under consideration by the U.S. House subcommittee could provide Tesla with significant advantages, potentially transforming its operational and financial landscape.

NHTSA Exemption Cap Increase

First, the proposed increase in the NHTSA exemption cap from 2,500 to 90,000 vehicles annually would allow Tesla to scale up development dramatically.

Currently, regulatory hurdles limit how many fully autonomous vehicles can hit the roads without exhaustive approvals. For Tesla, this means accelerating the rollout of its robotaxi fleet, which Musk envisions as a network of millions of vehicles generating recurring revenue through ride-hailing. With Tesla’s vast existing fleet of over 6 million vehicles equipped with FSD hardware, a higher cap could enable rapid conversion and deployment, turning parked cars into profit centers overnight.

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Preempting State Regulations

A united Federal framework would be created if it could preempt State regulations, eliminating the patchwork of rules that currently complicate interstate operations. Tesla has faced scrutiny and restrictions in states like California, especially as it has faced harsh criticism through imposed testing limits.

A federal override of State-level rules would reduce legal battles, compliance costs, and delays, allowing Tesla to expand services nationwide more seamlessly.

This is crucial for Tesla’s growth strategy, as it operates in multiple markets and aims for a coast-to-coast Robotaxi network, competing directly with Waymo’s city-specific expansions.

Bringing Safety Standards to the Present Day

Innovation in the passenger transportation sector has continued to outpace both State and Federal-level legislation, which has caused a lag in the development of many things, most notably, self-driving technology.

Updating these outdated safety standards, especially waiving requirements for steering wheels or mirrors, directly benefits Tesla’s innovative designs. Tesla wanted to ship Cybertruck without side mirrors, but Federal regulations required the company to equip the pickup with them.

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Cybercab is also planned to be released without a steering wheel or pedals, and is tailored for full autonomy, but current rules would mandate human-ready features.

Streamlined NHTSA reviews would further expedite approvals, addressing Tesla’s complaints about bureaucratic slowdowns. In a letter written in June to the Trump Administration, automakers, including Tesla, urged faster action, and this legislation could deliver it.

In Summary

This legislation represents a potential regulatory tailwind for Tesla, but it still relies on the government to put forth action to make things easier from a regulatory perspective. Enabling scale, innovation, and profitability in a sector that is growing quickly would benefit Tesla significantly, especially as it has established itself as a leader.

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Nvidia CEO Jensen Huang explains difference between Tesla FSD and Alpamayo

“Tesla’s FSD stack is completely world-class,” the Nvidia CEO said.

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Credit: Grok Imagine

NVIDIA CEO Jensen Huang has offered high praise for Tesla’s Full Self-Driving (FSD) system during a Q&A at CES 2026, calling it “world-class” and “state-of-the-art” in design, training, and performance. 

More importantly, he also shared some insights about the key differences between FSD and Nvidia’s recently announced Alpamayo system. 

Jensen Huang’s praise for Tesla FSD

Nvidia made headlines at CES following its announcement of Alpamayo, which uses artificial intelligence to accelerate the development of autonomous driving solutions. Due to its focus on AI, many started speculating that Alpamayo would be a direct rival to FSD. This was somewhat addressed by Elon Musk, who predicted that “they will find that it’s easy to get to 99% and then super hard to solve the long tail of the distribution.”

During his Q&A, Nvidia CEO Jensen Huang was asked about the difference between FSD and Alpamayo. His response was extensive:

“Tesla’s FSD stack is completely world-class. They’ve been working on it for quite some time. It’s world-class not only in the number of miles it’s accumulated, but in the way it’s designed, the way they do training, data collection, curation, synthetic data generation, and all of their simulation technologies. 

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“Of course, the latest generation is end-to-end Full Self-Driving—meaning it’s one large model trained end to end. And so… Elon’s AD system is, in every way, 100% state-of-the-art. I’m really quite impressed by the technology. I have it, and I drive it in our house, and it works incredibly well,” the Nvidia CEO said. 

Nvidia’s platform approach vs Tesla’s integration

Huang also stated that Nvidia’s Alpamayo system was built around a fundamentally different philosophy from Tesla’s. Rather than developing self-driving cars itself, Nvidia supplies the full autonomous technology stack for other companies to use.

“Nvidia doesn’t build self-driving cars. We build the full stack so others can,” Huang said, explaining that Nvidia provides separate systems for training, simulation, and in-vehicle computing, all supported by shared software.

He added that customers can adopt as much or as little of the platform as they need, noting that Nvidia works across the industry, including with Tesla on training systems and companies like Waymo, XPeng, and Nuro on vehicle computing.

“So our system is really quite pervasive because we’re a technology platform provider. That’s the primary difference. There’s no question in our mind that, of the billion cars on the road today, in another 10 years’ time, hundreds of millions of them will have great autonomous capability. This is likely one of the largest, fastest-growing technology industries over the next decade.”

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He also emphasized Nvidia’s open approach, saying the company open-sources its models and helps partners train their own systems. “We’re not a self-driving car company. We’re enabling the autonomous industry,” Huang said.

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