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Tesla FSD Beta 10.13’s improvements to be (especially) evident for non-CA users

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Elon Musk recently provided some updates on the wide rollout of FSD Beta 10.13. The latest iteration of the company’s advanced driver-assist system began its initial release earlier this month, but its wide release has not been initiated yet.

In a recent post on Twitter, Elon Musk admitted that while Tesla is working extremely hard on FSD Beta 10.13, the system itself is not ready for wide release just yet. He estimated that FSD Beta 10.13’s wide release would likely be a week or so away, but when it does, drivers outside California will experience some very notable improvements. 

As noted by the Tesla CEO, users “outside of California will notice improvements the most.” This is an interesting comment, especially considering that Musk has admitted in the past that FSD Beta “seems to work better in California” than in other areas such as Rhode Island. Musk admitted to this last year, noting that FSD Beta has been overfitted to the Bay Area

As noted in a Benzinga report, overfitting in statistics suggests that a model relates to a specific set of data points far too closely, which may not translate well with different data points. In the case of FSD Beta, this could result in the advanced driver assist system working very well in areas like San Francisco but not as well in other areas of the United States. 

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Release notes of FSD Beta 10.13 leaked earlier this month have revealed that the update includes a number of key performance improvements. 

Following are the partial release notes of FSD Beta 10.13 that have been shared thus far: 

  • Improved decision making for unprotected left turns using better estimation of ego’s interaction with other objects through the maneuver. 
  • Improved stopping pose while yielding for crossing objects at “Chuck Cook style” unprotected left turns by utilizing the median safety regions. 
  • Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects. 
  • Enabled creeping for visibility at any intersection where objects might cross ego’s path, regardless of presence of traffic controls. 
  • Improved lane position error by 5% and lane recall by 12%…
  • Improved lane position error of crossing and merging lanes by 22% by adding long-range skip connections and a more powerful trunk to the network architecture. 
  • Improved pedestrian and bicyclist velocity error by 17%, especially when ego is making a turn, by improving the onboard trajectory estimation used as input to the neural network. 
  • Improved animal detection recall by 34% and decreased false positives by 8% by doubling the size of the auto-labeled training set. 
  • Improved detection recall of far away crossing vehicles by 4% by tuning the loss function used during training and improving label quality.
  • Improved the “is parked” attribute for vehicles by 5% by adding 20% more examples to the training set. 
  • Upgraded the occupancy network to detect dynamic objects and improved performance by adding a video module, tuning the loss function, and adding 37k new clips to the training set. 
  • Reduced false slowdowns around crosswalks by better classification of pedestrians and bicyclists as not intending to interact with ego. 
  • Reduced false lane changes for cones or blockages by preferring gentle offsetting in-lane where appropriate. 
  • Improved in-lane positioning on wide residential roads. 
  • Improved object future path prediction in scenarios with high yaw rate. 
  • Improved speed limit sign accuracy on digital speed limits by 29%, on signs with difficult relevance by 23%, on 3-digit speeds by 39%, and on speed limit end signs by 62%. Neural network was trained with 84% more examples in the training set and with architectural changes which allocated more compute in the network head. 

Don’t hesitate to contact us with news tips. Just send a message to simon@teslarati.com to give us a heads up.

Simon is an experienced automotive reporter with a passion for electric cars and clean energy. Fascinated by the world envisioned by Elon Musk, he hopes to make it to Mars (at least as a tourist) someday. For stories or tips--or even to just say a simple hello--send a message to his email, simon@teslarati.com or his handle on X, @ResidentSponge.

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Tesla Summon got insanely good in FSD v14.3.2 — Navigation? Not so much

There were two new lines of improvements in the release notes: one addressing Actually Smart Summon (ASS), and another that now allows drivers to choose a reason for an intervention via a small menu during disengagement.

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(Photo: Hector Perez/YouTube)

Tesla Full Self-Driving v14.3.2 began rolling out to some owners earlier this week, and there are some notable improvements that came with this update.

There were two new lines of improvements in the release notes: one addressing Actually Smart Summon (ASS), and another that now allows drivers to choose a reason for an intervention via a small menu during disengagement.

Overall operation saw a handful of slight improvements, especially with parking performance, which has been the most notable difference with the arrival of FSD v14.3. However, there are still some very notable shortcomings, most notably with region-specific signage and navigation.

Tesla Assisted Smart Summon (ASS) improvements

There are noticeable improvements to ASS operation, which has definitely been inconsistent in terms of performance. Tesla wrote in the release notes for v14.3.2:

“Unified the model between Actually Smart Summon, FSD, and Robotaxi for more capable and reliable behavior.”
As recently as this month, I used Summon with no success. It had pulled around the parking lot I was in incorrectly, leaving the range at which Summon can be operated and losing a signal while moving in the middle of the lot.

This caused me to sprint across the lot to retrieve the vehicle:

Unfortunately, Summon was not dependable or accurate enough to use regularly. It appears Tesla might have bridged the gap needed to make it an effective feature, as two tests in parking lots proved that Summon was more responsive and faster to navigate to the location chosen.

It also did so without hesitation, confidently, and at a comfortable speed. I was able to test it twice at different distances:

I plan to test this more thoroughly and regularly through the next few weeks, and I avoided using it in a congested parking lot initially because I have not had overwhelming success with Summon in the past. I wanted to set a low baseline for it to see if it could simply pull up to the place I pinned in the Tesla app.

It was two for two, which is a big improvement because I don’t think I ever had successful Summon attempts back-to-back. It just seems more confident than ever before.

New Disengagement Categories

This is a really good idea from Tesla, but there are some issues with it. The categories you can select are Critical, Comfort, Preference, and Other.

I think the reasons why people choose to take over would be a better way to prompt drivers, like, “Traveling Too Fast,” “Incorrect Maneuver,” “Navigation Error,” would be more beneficial.

I say this because it seems that how we each categorize things might be different. For example, I shared a video of an intervention because the car had navigated to an exit to a parking lot and put its left blinker on, despite left turns not being allowed there.

I disengaged and chose Critical as the reason; it’s not a comfort issue, it’s not a preference, it’s quite literally an illegal turn, and it’s also dangerous because it cuts across several lanes of traffic and is 180 degrees.

Some said I should not have labeled this as Critical, but that’s the description I best characterized the disengagement as.

Categorizing interventions is a good thing, but it’s kind of hard to determine how to label them correctly.

Inconsistency with Regional Traffic Patterns

Tesla Full Self-Driving is pretty inconsistent with how it handles regional or local traffic patterns and road rules. The most frequent example I like to use is that of the “Except Right Turn” stop sign, which has become a notorious sighting on our social media platforms.

In the initial rollout of v14.3, my Model Y successfully navigated through one of these stop signs with no issues. However, testing at two of these stop signs yesterday proved it is still not sure how to read signs and navigate through them properly.

Off camera, I approached another one of these signs and felt the car coming to a stop, so I nudged it forward with the accelerator pedal pressed.

This helped the car go through the sign without stopping, but I could feel the bucking of the vehicle as the car really wanted to stop.

Musk said on the earnings call earlier this week that unsupervised FSD would probably be available in some regions before others, including a state-to-state basis in the U.S.

“It’s difficult to release this like to everyone everywhere all at once because we do want to make sure that they’re not unique situations in a city that particularly complex intersection or — actually, they tend to be places where people get into accidents a lot because they’re just — perhaps there’s — and like I said, an unsafe intersection or bad road markings or a lot of weather challenges. So I think we would release unsupervised gradually to the customer fleet as we feel like a particular geography is confirmed to be safe.”
This could be one of those examples that Tesla just has to figure out.

Highway Operation

Full Self-Driving is already pretty good at routine roadway navigation, so I don’t have too much to report here.

However, I was happy with FSD’s decision-making at several points, including its choice not to pass a slightly slower car and remain in the right lane as we approached the off-ramp:

Better Maneuvering at Stop Signs

Many FSD users report some strange operations at stop signs, especially four-way intersections where there is a stop sign and a line on the road, and they’re not even with one another.

I experienced this quite frequently and found that FSD would actually double stop: once at the stop sign and again at the line.

This created some interesting scenarios for me and I had many cars honk at me when the second stop would happen. Other vehicles that had waved me on to proceed through the intersection would become frustrated at the second stop.

FSD seems to have worked through this particular maneuver:

FSD should know to go to the more appropriate location (whichever provides better visibility), and proceed when it is the car’s turn to move. The double stop really ruined the flow of traffic at times and generally caused some frustration from other drivers.

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Tesla plans to resolve its angriest bunch of owners: here’s how

Since the rollout of the AI4 chip in Tesla vehicles, owners with the last generation self-driving chip, known as Hardware 3, have been persistent in their quest for a solution to their issue: they were told their cars were capable of unsupervised Full Self-Driving. It turns out the cars are not.

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Credit: Tesla Asia/Twitter

Tesla has a plan to make Hardware 3 owners whole after CEO Elon Musk admitted that those with that self-driving chip in their cars will not have access to unsupervised Full Self-Driving.

The company’s strategy is so crazy that it is sort of hard to believe.

Since the rollout of the AI4 chip in Tesla vehicles, owners with the last generation self-driving chip, known as Hardware 3, have been persistent in their quest for a solution to their issue: they were told their cars were capable of unsupervised Full Self-Driving. It turns out the cars are not.

During the Tesla Q1 earnings call on Wednesday, Musk finally clarified what the company’s plans are for Hardware 3 owners, what they will be offered, and what Tesla will have to do internally to prepare for it.

The answer was somewhat mind-boggling.

Musk said:

“Unfortunately, Hardware 3 — I wish it were otherwise, but Hardware 3 simply does not have the capability to achieve unsupervised FSD. We did think at one point it would have that, but relative to Hardware 4, it has only 1/8 of the memory bandwidth of Hardware 4. And memory bandwidth is one of the key elements needed for unsupervised FSD.”
He continued, stating that HW3 owners would have the opportunity to trade their cars in at a discounted rate in order to get the AI4 chip:

“So for customers that have bought FSD, what we’re offering is essentially a trade-in — like a discounted trade-in for cars that have AI4 hardware, and we’ll also be offering the ability to upgrade the car, to replace the computer. And you also need to replace the cameras, unfortunately, to go to Hardware 4.”
Obviously, Tesla has a lot of people to work with and make this whole thing right. Musk was adamant that HW3 would be capable of FSD, and now that the company has finally admitted that it is not, there are some things that could come of this.

There has been open talk about some sort of class action lawsuit against Tesla. The promises that Tesla made previously could be considered a breach of contract or even false advertising, and that’s according to Grok, Musk’s own AI program.

Musk went on to say that Tesla would likely have to establish new microfactories to effectively and efficiently replace HW3 computers and cameras:

…So to do this efficiently, we’re going to have to set up, like kind of micro factories or small factories in major metropolitan areas in order to do it efficiently. Because if it’s done just at the service center, it is extremely slow to do so and inefficient. So we basically need like many production lines to make the change.”
This is going to be an extremely costly process, especially if Tesla has to buy real estate, properties, and equipment to complete this work. Additionally, there was no wording on pricing, but Musk never said it would be free. It will likely come with some kind of price tag, and HW3 owners, after being left hanging for so long, will have something to say about that.

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SpaceX just got pulled into the biggest Weapons Program in U.S. history

SpaceX joins the Golden Dome software group, deepening its role in America’s most expensive defense program.

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US Golden Dome space defense system (Concept render by Grok)

SpaceX has joined a nine-company group developing the core operating software for the Golden Dome, America’s next-generation missile defense system. According to a Bloomberg report, SpaceX is focused on integrating satellite communications for military operations and is working alongside eight other defense and artificial intelligence companies, including Anduril Industries, Palantir Technologies, and Aalyria Technologies, to build software connecting missile defense capabilities.

The Golden Dome concept dates back to President Trump’s 2024 campaign, and on January 27, 2025, he signed an executive order directing the U.S. Armed Forces to construct the system before the end of his term. The system is planned to employ a constellation of thousands of satellites equipped with interceptors, with data centers in space providing automated control through an AI network.

FCC accepts SpaceX filing for 1 million orbital data center plan

Space Force Gen. Michael Guetlein, director of the Golden Dome initiative, has described the software layer as a “glue layer” that would enable officers to manage and control radars, sensors, and missile batteries across services. The consortium is aiming to test the platform this summer.

Trump selected a design in May 2025 with a $175 billion price tag, expected to be operational by the end of his term in 2029, though the Congressional Budget Office projected the cost could reach $831 billion over two decades.

The Golden Dome role is only the latest in a string of military wins for SpaceX. As Teslarati reported, the U.S. Space Force awarded SpaceX a $178.5 million task order on April 1, 2026 to launch missile tracking satellites for the Space Development Agency, covering two Falcon 9 launches beginning in Q3 2027. That came on top of more than $22 billion in government contracts held by SpaceX as of 2024, per CEO Gwynne Shotwell, spanning NASA resupply missions, classified intelligence satellites through its Starshield program, and military broadband.

The accumulation of defense contracts, now including a seat at the table on the most expensive weapons program in U.S. history, positions SpaceX as the dominant infrastructure provider for American national security in space. With a SpaceX IPO still on the horizon, each new contract adds weight to what is already one of the most consequential companies in aerospace history, raising real questions about how much of America’s defense architecture will depend on a single private operator before it ever trades publicly.

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