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Google’s DeepMind unit develops AI that predicts 3D layouts from partial images
Google’s DeepMind unit, the same division that created AlphaGo, an AI that outplayed the best Go player in the world, has created a neural network capable of rendering an accurate 3D environment from just a few still images, filling in the gaps with an AI form of perceptual intuition.
According to Google’s official DeepMind blog, the goal of its recent AI project is to make neural networks easier and simpler to train. Today’s most advanced AI-powered visual recognition systems are trained through the use of large datasets comprised of images that are human-annotated. This makes training a very tedious, lengthy, and expensive process, as every aspect of every object in each scene in the dataset has to be labeled by a person.
The DeepMind team’s new AI, dubbed the Generative Query Network (GQN) is designed to remove this dependency on human-annotated data, as the GQN is designed to infer a space’s three-dimensional layout and features despite being provided with only partial images of a space.
Similar to babies and animals, DeepMind’s GQN learns by making observations of the world around it. By doing so, DeepMind’s new AI learns about plausible scenes and their geometrical properties even without human labeling. The GQN is comprised of two parts — a representation network that produces a vector describing a scene and a generation network that “imagines” the scene from a previously unobserved viewpoint. So far, the results of DeepMind’s training for the AI have been encouraging, with the GQN being able to create representations of objects and rooms based on just a single image.
As noted by the DeepMind team, however, the training methods that have been used for the development of the GQN are still limited compared to traditional computer vision techniques. The AI creators, however, remain optimistic that as new sources of data become available and as improvements in hardware get introduced, the applications for the GQN framework could move over to higher-resolution images of real-world scenes. Ultimately, the DeepMind team believes that the GQN could be a useful system in technologies such as augmented reality and self-driving vehicles by giving them a form of perceptual intuition – extremely desirable for companies focused on autonomy, like Tesla.
Google DeepMind’s GQN AI in action. [Credit: Google DeepMind]
In a talk at Train AI 2018 last May, Tesla’s head of AI Andrej Karpathy discussed the challenges involved in training the company’s Autopilot system. Tesla trains Autopilot by feeding the system with massive data sets from the company’s fleet of vehicles. This data is collected through means such as Shadow Mode, which allows the company to gather statistical data to show false positives and false negatives of Autopilot software.
During his talk, Karpathy discussed how features such as blinker detection become challenging for Tesla’s neural network to learn, considering that vehicles on the road have their turn signals off most of the time and blinkers have a high variability from one car brand to another. Karpathy also discussed how Tesla has transitioned a huge portion of its AI team to labeling roles, doing the human annotation that Google DeepMind explicitly wants to avoid with the GQN.
Musk also mentioned that its upcoming all-electric supercar — the next-generation Tesla Roadster — would feature an “Augmented Mode” that would enhance drivers’ capability to operate the high-performance vehicle. With Tesla’s flagship supercar seemingly set on embracing AR technology, the emergence of new techniques for training AI such as Google DeepMind’s GQN would be a perfect fit for the next generation of vehicles about to enter the automotive market.
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Tesla Full Self-Driving shows stunning maneuver in Europe to silence skeptics
In a striking demonstration of autonomous driving prowess, Tesla’s Full Self-Driving (FSD) system recently showcased its capabilities on the narrow rural roads of the Netherlands. Captured in two in-car videos, the system encountered scenarios that would challenge even the most experienced human drivers.
Tesla Full Self-Driving, fresh on the heels of its approval for operation on European roads for the first time, showed off a stunning maneuver that will certainly silence any skeptics on the continent.
Fresh off its approval in the Netherlands, Full Self-Driving is working toward a significant expansion into more parts of Europe.
In a striking demonstration of autonomous driving prowess, Tesla’s Full Self-Driving (FSD) system recently showcased its capabilities on the narrow rural roads of the Netherlands. Captured in two in-car videos, the system encountered scenarios that would challenge even the most experienced human drivers.
In the first clip, a wide tractor occupied more than half the lane on a tight two-way road. Rather than braking abruptly or forcing a collision risk, FSD smoothly edged the vehicle onto the adjacent bike path—using the extra space with precision—before seamlessly returning to the lane once clear.
The second clip was equally demanding: while overtaking a group of cyclists, an oncoming car approached at speed.
FSD maintained a safe, minimal buffer to the cyclists while timing the pass perfectly, avoiding any swerve or hesitation that could unsettle passengers or other road users.
People wonder if FSD is safe on narrow European roads. Well have a look what it did when a tractor took up more than half of the road or when overtaking bicycles with fast oncoming traffic. pic.twitter.com/z37Csa09sP
— Chanan Bos (@ChananBos) April 14, 2026
This maneuver highlights FSD’s advanced spatial reasoning and predictive planning. On roads often under three meters wide, with no room for error, the system calculated available clearance in real time, incorporated shoulder and path geometry, and executed a controlled deviation without compromising safety.
It treated the bike path as a legitimate extension of navigable space, something many drivers might hesitate to do, while respecting Dutch road norms and cyclist priority.
Such feats align closely with a growing library of impressive FSD maneuvers documented on camera worldwide.
In urban Amsterdam, for instance, FSD has navigated the world’s densest cyclist environments, weaving through hundreds of unpredictable bike movements on canal-side streets with tram tracks and pedestrians.
One uncut drive showed it yielding smoothly at crossings, overtaking where needed, and even handling a near-perfect auto-park in a tight residential spot, demonstrating the same low-speed precision seen in the rural clips.
Teslas using FSD have tackled turbo roundabouts in the Netherlands, complex multi-lane circles notorious for geometry challenges, merging confidently while yielding to traffic. Similar clips depict smooth handling of construction zones, emergency vehicle pull-overs, and gated parking barriers, where the car stops precisely, waits for clearance, and proceeds without driver input.
Collectively, these examples illustrate FSD’s evolution toward handling the unpredictable.
The rural Netherlands maneuvers aren’t isolated. Instead, they reflect a pattern of spatial awareness, cyclist deference, and traffic anticipation seen from city streets to highways.
As FSD continues refining through real-world data, videos like this one are certainly building a compelling case for its readiness on Europe’s varied roads.
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Tesla utilizes its ‘Rave Cave’ for new awesome safety feature
Part of the massive interior overhaul of both the Model 3 “Highland” and Model Y “Juniper” was the addition of interior accent lighting to help bring out the mood of the vehicle, increase the customization of the interior, and to create a unique listening experience.
Tesla is utilizing its ‘Rave Cave’ for an awesome new safety feature that will arrive with the upcoming Spring Update for 2026.
Part of the massive interior overhaul of both the Model 3 “Highland” and Model Y “Juniper” was the addition of interior accent lighting to help bring out the mood of the vehicle, increase the customization of the interior, and to create a unique listening experience.
Tesla added a Sync Lights feature that will strobe the accent strips with the beat of the music.
It is one of the most unique and one of the coolest non-functional features of a Tesla, as it does not improve the driving of the vehicle, but makes it a cool and personal addition to the interior.
However, Tesla is going to take it one step further, as the Rave Cave lights will now be used for blind spot recognition. This feature will be added as the Spring 2026 Update starts to roll out.
A lot of CRAZY new features coming with Tesla’s 2026 Spring Update, including a new FSD app!
– Self-Driving App (AI4 hardware): New app in App Launcher > Self-Driving for one-tap FSD subscriptions, activation guides, and ongoing stats.
– “Hey Grok”: Voice-activated Grok with… https://t.co/ljeYPlq9Qt— TESLARATI (@Teslarati) April 13, 2026
Tesla writes:
“Accent lights now turn red when an object is in your blind spot and your turn signal is engaged, or when an approaching object is detected while parked.”
This neat new safety feature will now increase the likelihood of a driver, who is operating their Tesla manually, of seeing the blind spot warnings that are currently available on the A pillar and on the center touchscreen.
These new alerts will now warn drivers of cross traffic as they back out of a parking space with little to no visibility of what is coming. It is a great new addition that will only increase the safety of the vehicles, while also utilizing something that is already installed in these specific Model 3 and Model Y units.
The Model 3 and Model Y were the central focus of the Spring 2026 Update, especially considering the fact that the Model S and Model X are basically gone, with only a few hundred units left. Additionally, Tesla included new Immersive Sound and Car Visualization for the Model 3 and Model Y specifically in this new update.
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Tesla parked 50+ Cybercabs outside its Texas Factory with some crash tested
Dozens of Tesla Cybercabs have been spotted at Giga Texas crash testing facility ahead of launch.
Drone footage captured by longtime Giga Texas observer Joe Tegtmeyer shows over 50 units of Tesla Cybercab at the Austin factory campus, including several units clustered by Tesla’s on-site crash testing facility.
The outbound lot at Gigafactory Texas sits just outside the factory exit and serves as the primary staging area where finished vehicles are held before being loaded onto transport carriers or dispatched for validation testing. On any given day, the lot holds a mix of Model Y and Cybertruck units alongside the growing Tesla Cybercab fleet, as can be seen in the drone footage captured by Joe Tegtmeyer.
Tesla Cybercab fleet spotted at Gigafactory Texas on April 13, 2026 [Credit: Joe Tegtmeyer)
Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)
Tesla Cybercab crash test units spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)
Tesla Full Self-Driving shows stunning maneuver in Europe to silence skeptics
Tesla utilizes its ‘Rave Cave’ for new awesome safety feature
