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SpaceX’s Starship Moon lander passes NASA review alongside Blue Origin, Dynetics

Pictured on the left, SpaceX's lunar Starship is a customized version of the baseline ship meant to land NASA astronauts on the Moon. (SpaceX)

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A variant of SpaceX’s Starship spacecraft optimized to land NASA astronauts on the Moon has passed the space agency’s first review alongside competing teams lead by Blue Origin and Dynetics.

Aside from reiterating the fact that NASA is drawing heavily from its experience with the Commercial Crew Program (CCP), the completion of “certification baseline reviews” for Blue Origin, Dynetics, and SpaceX’s proposed lunar landers is a significant step forward for the Human Landing System (HLS) and Artemis programs. According to NASA’s official HLS “Broad Agency Announcement” or BAA, providers must submit a vast amount of paperwork and data to pass the certification baseline review (CBR).

NASA’s acceptance criteria for CBR documentation is about as general as the space agency gets, requiring providers to demonstrate at least a basic level of maturity and expertise. Like the name suggests, it sets a baseline from which NASA and SpaceX, Dynetics, and Blue Origin’s National Team will hone in on challenges and concerns specific to each system. SpaceX’s proposal is almost certainly unique, however, given that the company is the only one anywhere close to performing actual flight tests of a (relatively) similar system.

Pictured on the left, SpaceX’s lunar Starship is a customized version of the baseline ship meant to land NASA astronauts on the Moon. (SpaceX)

After much fanfare, NASA finally revealed its first real Human Landing System contracts on April 30th, 2020, awarding funds to Blue Origin, Dynetics, and SpaceX to develop three extremely dissimilar Moon landers. Designed to ferry NASA astronauts from a deserted lunar orbit (near-rectilinear halo orbit, NRHO). NASA initially refused to delineate the distribution of the $967 million contract.

A list of the HLS Certification Baseline Review (CBR) “acceptance criteria and products”. (NASA)

Several news outlets later reported that Blue Origin’s “National Team” (including Draper, Lockheed Martin, and Northrop Grumman) received $567 million to develop a complex three-stage system, using Blue Origin’s existing Blue Moon lander work for the final descent stage and lander. Dynetics won $253 million to build a slightly more familiar single-stage lander and SpaceX received $135 million for a single-stage Starship-derived vehicle.

The main goal of NASA’s initial funding is to extensively characterize and understand the capabilities and characteristics of each proposal and the likelihood that each vehicle will actually be ready to land humans on the Moon by the end of 2024. The next major HLS milestone will be what the space agency calls a “continuation review,” in which NASA will likely downselect to one of the three landers above. Administrator Jim Bridenstine says that NASA may decide to proceed with more than one provider but the strong implication is that only one will exit the ~December 2020 continuation review with future funding.

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(SpaceX)
Unlike Blue Origin and Dynetics, SpaceX has already flight-tested multiple full-scale Starship prototypes. (SpaceX)

For SpaceX, it appears that the company will almost certainly field an orbit-capable Starship and Super Heavy booster with or without external help. At this point in the program, it would take a major upset for SpaceX not to be ready to start orbital Starship launch attempts in 2021. To an extent, SpaceX has proven through Falcon 9, Falcon Heavy, and Crew Dragon that it’s capable of developing reliable, reusable, industry-leading rockets and spacecraft several times more cheaply than its closest competitors.

To build a Starship safe and reliable enough that SpaceX can convince NASA to land astronauts on the Moon with it, the company will effectively have to prove that it can cut the cost of rocket production by another factor of five or ten. Time will tell where NASA’s HLS cards fall just a few months from now.

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Eric Ralph is Teslarati's senior spaceflight reporter and has been covering the industry in some capacity for almost half a decade, largely spurred in 2016 by a trip to Mexico to watch Elon Musk reveal SpaceX's plans for Mars in person. Aside from spreading interest and excitement about spaceflight far and wide, his primary goal is to cover humanity's ongoing efforts to expand beyond Earth to the Moon, Mars, and elsewhere.

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Tesla’s Navigation Nightmare: Why the easiest part of FSD might be the hardest

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Credit: TESLARATI

Turn-by-turn navigation is not new technology.

For over two decades, drivers have relied on Garmin, TomTom, and later smartphone apps like Google Maps and Waze to receive precise, reliable directions. These systems have guided millions safely through unfamiliar cities, highways, and backroads with remarkable effectiveness. They handle real-time traffic, construction detours, and complex intersections with minimal fuss.

Yet Tesla, the company that promised revolutionary Full Self-Driving (FSD), continues to struggle with this foundational capability. As FSD (Supervised) v14.3.4 has started rolling out to cars this week, navigation remains its glaring Achilles’ heel, undermining the entire autonomous vision.

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

Tesla’s FSD excels in many driving behaviors—smooth acceleration, confident lane changes in ideal conditions, and responsive handling of visible obstacles. However, when it comes to following a route accurately, the system falters repeatedly.

Owners report wrong turns, missed exits, inefficient routing through local roads instead of highways, phantom speed limit errors, and even directing vehicles to building rear entrances. Interventions for navigation issues often outnumber those for core driving maneuvers. Tesla has begun surveying owners specifically about these errors, acknowledging the problem after years of complaints.

Navigation is perhaps my biggest complaint when it comes to FSD, because sometimes, we do know better. Some of us have been living in our areas for our entire lives, but even those who have not have years or even decades of experience driving on local roads. We might know a little better about routing.

But the navigation mistakes are more than just FSD potentially taking a slightly different route that may or may not save you a few minutes. Sometimes, they’re genuinely mind-boggling.

This isn’t just annoying; it cascades into broader failures. A flawed route plan confuses the AI’s decision-making, leading to hesitant behavior, unnecessary disengagements, or dangerous maneuvers like attempting impossible U-turns or ignoring clear ramps. In a system meant to operate with minimal supervision, unreliable navigation erodes trust.

More often than not, false or plain incorrect navigation is what causes me to interrupt FSD operation. Unfortunately, I believe the latest FSD version is the worst example of it, and it leads me to believe that Tesla might be making some changes; they’ve just made them in the wrong direction.

It makes you wonder: Why is a company that has done so much with the progress of FSD and autonomy struggling so much with navigation, something that is not new and has been around a long time?

Multiple Data Sources

First, Tesla’s navigation relies on a fragile patchwork of multiple data sources—Google Maps, TomTom, OpenStreetMap, Valhalla, and its own fleet-derived data—stitched together rather than a single authoritative map. When these conflict on lane geometry, road status, or turn details, the system hesitates or chooses incorrectly.

Traditional GPS providers maintain centralized, regularly validated databases with professional curation and rapid updates. Tesla’s hybrid approach, while innovative in crowdsourcing, introduces inconsistencies that a purely vision-based or end-to-end AI approach may not easily reconcile in real time.

Persistent Learning

FSD seems to struggle with persistent learning from driver interventions.

Unlike consumer apps that quickly adapt to repeated corrections or user preferences (e.g., avoiding certain routes or remembering habitual detours), Tesla’s FSD often fails to internalize fixes on the same trip or across similar scenarios. Owners note making the same manual override multiple times without the routing engine updating its behavior meaningfully.

This stems from the neural architecture prioritizing real-time perception and control over long-term route memory and personalization, making navigation feel rigid and “opinionated” compared to the adaptive logic in Waze or Google Maps.

I noticed that when I asked Grok to try and get me home a certain way (a way that FSD routinely took in the past because it was the most efficient), it had to place a waypoint between my location at the time and my house. When I went to edit the waypoint out, as Grok had placed it for a way to get FSD to get off the highway at the right exit, it was stumped again, rerouted, and took a longer way home.

Reasoning, Scaling, and Intuition

Third, scaling navigation for unsupervised or robotaxi ambitions requires not just accuracy but adaptability and user-like reasoning. Current FSD often defaults to single routes that ignore driver preferences or real-world nuances like time-of-day traffic patterns. It fails to match the intuitive, context-aware planning that traditional systems have refined over the years.

Resolving navigation is critical for several reasons. Practically, it is the backbone of any autonomous journey: without trustworthy routing, the car cannot reliably reach destinations, rendering FSD useless for robotaxis or hands-free commutes. Safety depends on it—mismatched plans create hesitation in merges or intersections, increasing accident risk.

Economically, Tesla’s valuation and future hinge on FSD delivering unsupervised driving; persistent navigation flaws delay regulatory approval and erode consumer confidence. For owners who paid premiums for FSD, these issues represent unfulfilled promises. While it is unlikely Tesla will lose too many customers due to bad navigation, some will be frustrated with the constant need for human input.

Tesla has achieved miracles in electric vehicles and battery tech. Mastering turn-by-turn—technology Garmin nailed in the early 2000s—should not be this hard. By investing in tighter data integration, faster learning loops from interventions, and more intuitive routing algorithms, Tesla could close this gap.

Until then, FSD’s navigation struggles highlight a humbling truth: even the most ambitious innovator must sometimes master the basics before conquering the future.

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Tesla Cybertruck driver gets pickup seized for ‘legitimate concerns’ in UK

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A Tesla Cybertruck driver in the United Kingdom had their all-electric pickup seized by local police in the Greater Manchester area after the department cited “legitimate concerns.”

Last Thursday, police saw the pickup on the roads and decided to pull the driver over. Greater Manchester Police said:

“Whilst this may seem trivial to some, legitimate concerns exist around the safety of other road users or pedestrians if they were involved in a collision with the Cybertruck.”

The Cybertruck in question was, according to the BBC, registered and insured abroad and was confiscated. The driver, who is a UK resident, was reported.

The Greater Manchester Police Department then added:

“The Tesla Cybertruck is not road-legal in the UK and does not hold a certificate of conformity.”

The Cybertruck cannot be legally driven in the UK because it has no UK Type Approval for operation in the country. This is due to some safety concerns, which are related to its angular shape and design. The stainless steel exoskeleton has sharp edges and projections that violate UK/EU rules on pedestrian protection.

Tesla has considered creating what it referred to as an “international version” that would be approved for operation in Europe. However, there has been no real movement on that front by the company, as it has been focused on the Robotaxi rollout primarily.

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Apple is developing the missing link for Tesla to get CarPlay: report

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Credit: Michał Gapiński/YouTube

A new report claims that Apple is in the process of developing what would be the missing link for Tesla to get CarPlay.

Apple and Tesla have been reportedly working together for some time to give Tesla owners the opportunity to utilize CarPlay within their vehicles. While many owners are more than happy with Tesla’s in-house UI, which is seamless, effective, and smooth, some still want CarPlay, which does have its advantages.

A report from 9to5Mac now states that a new CarPlay technology that was highlighted during the Worldwide Developers Conference (WWDC) would potentially be the bridge between Tesla and Apple. With the addition of a feature known as “Route Sharing,” which gives a navigation app the ability to share routing data with the vehicle, Tesla would be able to launch CarPlay in its vehicles, the report states.

CarPlay has not been a priority for Tesla because it has done extremely well with its in-house UI, but some drivers are just used to it. Additionally, it could improve Tesla’s subpar Navigation or offer improved app capabilities, especially with iMessage.

Route Sharing is an intended addition to CarPlay’s iteration in iOS 26.4, which was released in March:

The addition of CarPlay would undoubtedly be welcome, but at the same time, it seems like Tesla realizes it is not of the utmost priority. There are so many things that Tesla is working on currently within its own vehicles, especially attempting to solve self-driving.

Back in February, Bloomberg had reported that Tesla was still working on bringing CarPlay to its vehicles, but it had not due to app compatibility issues and incredibly low adoption rates of iOS 26.

This bottleneck could buy Tesla the proper amount of time to develop CarPlay for its vehicles. It would be a welcome addition, and could be brought on with either the Summer or Fall 2026 Software Updates.

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