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

Elon Musk

Elon Musk reveals unfortunate truth of Tesla Full Self-Driving development

In a candid reply to a dramatic video of Tesla’s Full Self-Driving (FSD) system averting disaster, Elon Musk laid bare a harsh reality facing autonomous vehicle technology.

Published

on

Tesla’s Full Self-Driving suite is one of the most significant technological developments in terms of passenger travel in decades, but it is not all sunshine and rainbows, even with major strides in safety, CEO Elon Musk revealed.

In a candid reply to a dramatic video of Tesla’s Full Self-Driving (FSD) system averting disaster, Elon Musk laid bare a harsh reality facing autonomous vehicle technology.

The clip shows a Model 3 traveling at over 65 mph on a foggy, rain-soaked highway when a pedestrian suddenly steps into traffic.

Full Self-Driving instantly detects the threat and swerves safely, preventing what could have been a fatal collision for both the pedestrian and the driver’s cousin.

Advertisement

Musk’s response was unequivocal:

“Tesla self-driving saves a lot of lives – the statistics are unequivocal. That doesn’t mean it’s perfect, of course.” Even with a projected 10x safety improvement over human drivers, FSD would still prevent roughly 90% of the world’s approximately one million annual auto fatalities. The remaining 10%—roughly 100,000 deaths—would expose Tesla to relentless lawsuits. Meanwhile, the vast majority of lives saved would go unnoticed. “The 90% who are still alive mostly won’t even know that Tesla saved them. Nonetheless, it is the right thing to do.”

This “unfortunate truth,” as Musk implicitly framed it, highlights a fundamental asymmetry in how society perceives safety technology. Human drivers cause the overwhelming majority of crashes through distraction, fatigue, or error.

Yet when FSD errs, the incident becomes headline news and a courtroom target. Prevented tragedies, by contrast, leave no trace.

Advertisement

Survivors simply continue their journeys, unaware of the split-second intervention that kept them alive. The result is a distorted public narrative that amplifies failures while rendering successes invisible.

We have seen this through various headlines throughout the years, including the mainstream media’s obsession with only mentioning the manufacturer’s name in the instance of an accident when it is “Tesla.”

Opinion: Tesla Autopilot NHTSA investigation headlines are out of control

The video’s real-world example underscores FSD’s current capabilities. In near-zero visibility, the system’s cameras and neural network reacted faster than any human could, demonstrating the life-saving potential Musk cites.

Advertisement

Tesla’s latest safety data already shows FSD (Supervised) performing significantly better than the U.S. average, with crashes occurring far less frequently per mile driven.

Still, regulatory scrutiny, liability concerns, and media focus on edge-case failures continue to slow widespread adoption. Musk’s frank admission suggests Tesla is prepared to push forward despite the legal and perceptual headwinds.

As FSD edges closer to unsupervised autonomy, Musk’s post serves as both a progress report and a reality check. The technology is already saving lives today.

The unfortunate truth is that proving it and scaling it responsibly will require society to value statistical lives saved as much as dramatic stories of those lost. In the race toward safer roads, perception may prove as formidable an obstacle as the fog and rain in that viral video.

Advertisement
Continue Reading

News

Tesla Full Self-Driving v14.3: First Impressions

Published

on

Tesla started rolling out Full Self-Driving v14.3 to Early Access Program (EAP) members earlier today, and I had the opportunity to see some of the improvements that were made from v14.2.2.5.

While a lot of things got better, and I truly enjoyed using Full Self-Driving again after being stuck with the widely confusing and frustrating v14.2.2.5, Tesla still has one major problem on its hands, and it has to do with Navigation and Routing. I truly believe those issues will be the biggest challenges Tesla will face with autonomy: the car simply going the correct way, not conflicting with what the navigation says, and taking the simplest and most ideal route to a destination.

Here’s what I noticed as an improvement with my first hour with v14.3. This is not a full review, nor is it reflective of everything I will likely experience with this new version. This is simply what I saw as a noticeable improvement from the past version, v14.2.2.5.

There is also a more streamlined version on X, available at the thread below:

Advertisement

Yellow Light Behavior is Significantly Better

On v14.2.2.5, I had so many instances of the car slamming the brakes on to stop at a yellow light when it was clearly the safer option to proceed through. There were several times when the car would be about 20 feet from the line, traveling at 15-20 MPH, the light would turn yellow, and it would slam the brakes to stop. I would nudge it through yellow lights constantly because of this by putting my foot on the accelerator.

The instances I’m talking about here would not have been close calls — the car would have likely moved through the intersection completely before the light would turn red.

Advertisement

On multiple occasions this evening, FSD proceeded through yellow lights safely, without hesitation or any brake stabbing. It was refreshing:

This was a huge complaint with v14.2.2.5. Sometimes, it’s a safer option to go through a yellow light, especially when you have traffic behind you. It’s a great way to get rear-ended.

Parking Performance

I had four instances of parking, and FSD v14.3 really did a flawless job. I was very impressed with how solid it was, but also with how efficiently it moved into the spot. When there was traffic around with past versions, I usually chose to park manually just because FSD took its time getting into a spot. I don’t see that being an issue anymore.

I complained about parking a lot and shared several images on X and Facebook of those examples:

Advertisement

No issues with it this evening. 4/4. Here are two looks:

Highway Performance

FSD v14.3 passed the five cars shown in this image:

Advertisement

The sixth was 200-300 yards ahead of the fifth. In v14.2.2.5, FSD would usually stay in the left lane, especially on Hurry and Mad Max. It did not do that, as it instead chose to get back over in the right lane after passing the final car.

Speed was not much of a concern here, even though it was going 21 MPH over. Although it was fast, I did have a line of cars behind me traveling at the same speed, and FSD had just merged about a half mile prior, so I chose to let it continue.

There were no instances of camping in the left lane for extended periods of time. I do want to do more testing with the Speed Profiles because they were in need of some work with the previous version. I am starting to side with those who want a Max Speed setting, which was removed last year.

Navigation and Routing Still Need Work

I was heading back toward where I came from, so I turned “Avoid Highways” on to take a different way. This confused the Routing system, and instead of turning left, then right, as the Routing said, the car turned right, then indicated for another right, basically going in a big rectangle. The car ignored the second right-hand turn and continued straight. I ended up turning “Avoid Highways” off and letting the car pick the same routing option as what took me here.

Advertisement

I have truly complained so much about Navigation and Routing that I’m starting to feel sort of bad. It is obviously such a massive challenge for some reason, but I am confident it will improve. I recall seeing Tesla hiring someone for this role a few months back, so perhaps there is hope for it to get better.

Smarter Behavior When Approaching Exits/Routing

This probably should be grouped in with Highway Behavior, but I wanted to highlight it on its own.

The highway exit pictured was always frustrating for v14.2.2.5. In the Hurry speed profile, I have seen it try to execute passes on multiple cars with as little as 0.6 miles to spare before taking the exit.

With three cars ahead of it, it chose to reduce speed and just wait until the exit. It was refreshing to see an improvement here, so I hope this behavior persists. Sometimes there’s just no reason to pass when you’re less than a mile from getting off the highway anyway.

Advertisement

Larger Visibility Warnings

Tesla seems to have increased the size of these “Camera Visibility Limited” warnings. Previously, they were just small thumbnails:

Advertisement

Stop Sign Behavior

This is probably the biggest improvement of all, because how it behaved at Stop Signs in v14.2.2.5 was so incredibly terrible and disruptive to the flow of a busy intersection.

There are several four-way, all-stop intersections near me. In the past, FSD would stop well behind the Stop Sign or the white-painted line on the road. It would then inch forward, stopping again at this line, essentially making two stops at a single intersection.

If there is visibility, I don’t truly care where FSD stops, as long as it stops once. Stopping twice just isn’t ideal or logical. I can’t imagine many humans would do it, I know I wouldn’t.

I didn’t have that issue this evening:

Advertisement

This was pretty tight, too, in the sense that both my car and the other one got to the intersection at the same time. FSD may have stopped first, but the other vehicle was probably around the same point that I was when FSD decided to stop. I was happy to see the assertiveness to proceed; it felt like it was ideal to just go through. I was happy it didn’t stop a second time up at the line. I’d be fine if it stopped at the line, as long as that was the only stop it made.

Advertisement
Continue Reading

News

Tesla Full Self-Driving v14.3 rolls out: here’s what’s new

We are in EAP and will be on the road with v14.3 in the coming hours, so we’ll have a lot of things to discuss over the next few days, especially coming from v14.2.2.5, which I called the most “confusing” FSD release of all time.

Published

on

Tesla has officially started rolling out Full Self-Driving v14.3 to Early Access Program (EAP) members, and there are a lot of new improvements.

We are in EAP and will be on the road with v14.3 in the coming hours, so we’ll have a lot of things to discuss over the next few days, especially coming from v14.2.2.5, which I called the most “confusing” FSD release of all time.

Tesla brought out a lot of improvements, according to the v14.3 release notes, which list a vast number of fixes, new features, and new capabilities.

Here’s what Tesla’s release notes for the v14.3 release state:

Advertisement
  • Improved parking location pin prediction, now shown on a map with a P icon.
  •  Increased decisiveness of parking spot selection and maneuvering.
  • Rewrote the Al compiler and runtime from the ground up with MLIR, resulting in 20% faster reaction time and improving model iteration speed.
  • Enhanced response to emergency vehicles, school buses, right-of-way violators, and other rare vehicles.
  • Mitigated unnecessary lane biasing and minor tailgating behaviors.
  • Improved handling of small animals by focusing RL training on harder examples and adding rewards for better proactive safety.
  • Improved traffic light handling at complex intersections with compound lights, curved roads, and yellow light stopping – driven by training on hard RL examples sourced from the Tesla fleet.
  • Upgraded the Reinforcement Learning (RL) stage of training the FSD neural network, resulting in improvements in a wide variety of driving scenarios.
  • Upgraded the neural network vision encoder, improving understanding in rare and low-visibility scenarios, strengthening 3D geometry understanding, and expanding traffic sign understanding.
  • Improved handling for rare and unusual objects extending, hanging, or leaning into the vehicle path by sourcing infrequent events from the fleet.
  • Improved handling of temporary system degradations by maintaining control and automatically recovering without driver intervention, reducing unnecessary disengagements.

Tesla also listed a handful of future improvements as well:

  • Expand reasoning to all behaviors beyond destination handling
  • Add pothole avoidance
  • Improve driver monitoring system sensitivity with better eye gaze tracking, eye wear handling, and higher accuracy in variable lighting situations

CEO Elon Musk has said that v14.3 could be “where the last big piece of the puzzle finally lands.” We have high expectations for this release because, in a lot of ways, v14.2’s final version was extremely disappointing and seemed to be a regression more than anything.

Nevertheless, Full Self-Driving v14.3 is going to be quite an interesting test, considering this is also the first time Musk has stated it will feel like the car will be “sentient.”

Reasoning will be a bigger piece of the puzzle with this release, although there were some elements of it in v14.2.

Tesla AI Head says future FSD feature has already partially shipped

We plan to travel plenty of miles with it over the next few days, so we’ll keep you posted on what our thoughts are.

Advertisement
Continue Reading