Connect with us

News

Tesla Autopilot and artificial intelligence: The unfair advantage

Published

on

Serial tech entrepreneur and Tesla CEO Elon Musk has had a longstanding fear of artificial intelligence, but his company’s investments in artificial intelligence have been noted as an attempt to keep track of developments in the field of AI. In an interview for Vanity Fair in April 2017, he outright expressed his concerns with AI and claimed that one of the reasons for the development of SpaceX was that it could be an interplanetary escape route for humanity if artificial intelligence goes rogue. However, even Musk realizes the importance of AI in real-world applications, specifically for self-driving cars. At the end of June, Musk hired Andrej Karpathy as the new Director of Artificial Intelligence at Tesla, and MIT Technology Review claims it is the start of a plan to rethink automated driving at Tesla.

Karpathy comes from OpenAI, a non-profit company founded by Musk that focuses on “discovering and enacting the path to safe artificial general intelligence.” Afterwards, he moved on to intern at DeepMind, a place that spotlighted reinforcement learning with AI. Karpathy’s previous research focuses are on image understanding and recognition, which directly translates into applying proven image recognitions algorithms in Tesla’s Autopilot.

Recently, the popular question of morality was brought up in context to AI learning in Autopilot cars. It’s very interesting to consider how to teach technology to respond to an innately human moral problem. The Moral Machine, hosted by Massachusetts Institute of Technology, is a platform built to “gather human perspectives on moral decisions made by machine intelligence, such as self-driving cars.” It questions how the machine would act in human decisions such as whether to crash the driver or keep driving into a pedestrian that is crossing the street where there are no traffic regulators. How exactly do you teach a logical machine the mechanisms of ethical decision-making?

Although Musk and Tesla are the leaders in the self-driving field, a number of other companies are also entering into the competition sphere. Google, Uber, and Intel’s Mobileye have all been considering the application of reinforcement learning in the context of self-driving cars. Uber, Waymo, GM (Cruise Automation), Mobileye (camera supplier), Mercedes and Velodyne (LiDAR Supplier) could be potential competitors in the realm of self-driving vehicles. However, most of the technology does not encompass full self-driving, which is Musk’s aim. While other companies are investing heavily in autonomous fleets, Tesla far outpaces them in terms of data collection and release of finished product.

Advertisement

What are the differentiators for Tesla in the growing field of AI directed driverless cars?

Historically, Musk has focused on “narrow AI” which can enable the car to make decisions without driver interference. The vehicles would increasingly rely on radar as well as ultrasonic technology for sensing and data-gathering to form the basis for Tesla’s Autopilot algorithms. A technology that isn’t derived from LiDAR, the combination of radar and camera system said to outperform LiDAR especially in adverse weather conditions such as fog.

With the introduction of Autopilot 2.0 and Tesla’s “Vision” system, and billions of miles real-world driving data collected by Model S and Model X drivers, Tesla continues to create a detailed 3D map of the world that has increasingly finer resolution as more vehicles are purchased, delivered and placed onto roadways. The addition of GPS allows Tesla to put together a visual driving map for AI vehicles to follow, paving the path for newer and more advanced vehicles.

The addition of Karpathy will be a notable asset for Tesla’s Autopilot team. In specific, the team will be able to apply Karpathy’s deep knowledge of reinforcement learning systems. Reinforcement learning for AI is similar to teaching animals via repetition of a behavior until a positive outcome is yielded. This type of machine learning will allow Tesla Autopilot to navigate complex and challenging scenarios. For example, AI will allow cars to determine in real-time how to navigate a four-way stop, a busy intersection or other difficult situations present on city streets. By making cars smarter with the way they navigate drivers, Tesla will put itself ahead of the curve with a fully-thinking, fully self-driving car.

Tesla is expected to demonstrate a fully autonomous cross-country drive from California to New York by the end of this year as a showcase for its upcoming Full Self-driving Capability. If you’re buying a Tesla Model 3, or an existing Model S or Model X owner, just know that you’re contributing to a self-driving future, mile by mile.

Advertisement
Advertisement
Comments

News

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.

Published

on

Credit: Tesla

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.

Advertisement

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.

Advertisement

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.

Advertisement

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.

Advertisement

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.

Continue Reading

News

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.

Published

on

Credit: Tesla | X

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.

Advertisement

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.

Advertisement

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.

Advertisement

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.

Continue Reading

News

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.

Published

on

By

Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)
Tesla Cybercab fleet spotted at Gigafactory Texas on April 13, 2026 [Credit: Joe Tegtmeyer)

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 [Credit: Joe Tegtmeyer)

Tesla Cybercab fleet spotted at Gigafactory Texas on April 13, 2026 [Credit: Joe Tegtmeyer)

Roughly 50 Cybercab units are visible across the campus, parked in tight organized rows. Most of the units visible still carry steering wheels and pedals, temporary additions Tesla included to satisfy current safety regulations while the vehicles accumulate real-world data ahead of full regulatory approval for a steering wheel-free design.

Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

Tesla Cybercab fleet spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

Tesla operates dedicated Crash Labs at both its Giga Texas and Fremont facilities that are purpose-built for controlled structural crash tests. Historically, automakers begin intensive crash testing roughly one to two months before volume production kicks off. The Cybertruck followed almost exactly that pattern. The Cybercab appears to be on the same track facility that we first saw back in October 2025.

Tesla Cybercab crash test units spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

Tesla Cybercab crash test units spotted at Gigafactory Texas [Credit: Joe Tegtmeyer)

The first production Cybercab rolled off the Giga Texas line on February 17, 2026. Volume production is now targeted for April. Musk previously wrote on X that “the early production rate will be agonizingly slow, but eventually end up being insanely fast,” and separately stated Tesla is targeting at least 2 million Cybercab units per year. Commercial robotaxi service in Austin is targeted for late 2026.

 

Advertisement
Continue Reading