Breaking Down Tesla’s Autopilot vs. Wall “Wile E. Coyote” Video

By Not a Tesla App Staff
Mark Rober

Mark Rober, of glitter bomb package fame, recently released a video titled Can You Fool A Self-Driving Car? (posted below). Of course, the vehicle featured in the video was none other than a Tesla - but there’s a lot wrong with this video that we’d like to discuss.

We did some digging and let the last couple of days play out before making our case. Mark Rober’s Wile E. Coyote video is fatally flawed.

The Premise

Mark Rober wanted to prove whether or not it was possible to fool a self-driving vehicle, using various test scenarios. These included a wall painted to look like a road, low-lying fog, mannequins, hurricane-force rain, and bright beams.

All of these individual “tests” had their own issues - not least because Mark didn’t adhere to any sort of testing methodology, but because he was looking for a result - and edited his tests until he was sure of it.

Interestingly, many folks on X were quick to spot that Mark had been previously sponsored by Google to use a Pixel phone - but was using an iPhone to record within the vehicle - which he had edited to look like a Pixel phone for some reason. This, alongside other poor edits and cuts, led many, including us, to believe that Mark’s testing was edited and flawed.

Flaw 1: Autopilot, Not FSD

Let’s take a look at the first flaw. Mark tested Autopilot - not FSD. Autopilot is a driving aid for lane centering and speed control - and is not the least bit autonomous. It cannot take evasive maneuvers outside the lane it is in, but it can use the full stable of Tesla’s extensive features, including Automatic Emergency Braking, Forward Collision Warnings, Blind Spot Collision Warnings, and Lane Departure Avoidance.

On the other hand, FSD is allowed and capable of departing the lane to avoid a collision. That means that even if Autopilot tried to stop and was unable to, it would still impact whatever obstacle was in front of it - unlike FSD.

As we continue with the FSD argument - remember that Autopilot is running on a 5-year-old software stack that hasn’t seen updates. Sadly, this is the reality of Tesla not updating the Autopilot stack for quite some time. It seems likely that they’ll eventually bring a trimmed-down version of FSD to replace Autopilot, but that hasn’t happened yet.

Mark later admitted that he used Autopilot rather than FSD because “You cannot engage FSD without putting in a destination,” which is also incorrect. It is possible to engage FSD without a destination, but FSD chooses its own route. Where it goes isn’t within your control until you select a destination, but it tends to navigate through roads in a generally forward direction.

The whole situation, from not having FSD on the vehicle to not knowing you can activate FSD without a destination, suggests Mark is rather unfamiliar with FSD and likely has limited exposure to the feature.

Let’s keep in mind that FSD costs $99 for a single month, so there’s no excuse for him not using it in this video.

Flaw 2: Cancelling AP and Pushing Pedals

Many people on X also followed up with reports that Mark was pushing the pedals or pulling on the steering wheel. When you tap on the brake pedal or pull or jerk the steering wheel too much, Autopilot will disengage. For some reason, during each of his “tests,” Mark closely held the steering wheel of the vehicle.

This comes off as rather odd - at the extremely short distances he was enabling AP at, there wouldn’t be enough time for a wheel nag or takeover warning required. In addition, we can visibly see him pulling the steering wheel before “impact” in multiple tests.

Over on X, techAU breaks it down excellently on a per-test basis. Mark did not engage AP in several tests, and he potentially used the accelerator pedal during the first test - which means that Automatic Emergency Braking is overridden. In another test, Mark admitted to using the pedals.

Flaw 3: Luminar Sponsored

This video was potentially sponsored by a LiDAR manufacturer - Luminar. Although Mark says that this isn’t the case. Interestingly, Luminar makes LiDAR rigs for Tesla - who uses them to test ground truth accuracy for FSD. Just as interesting, Luminar’s Earnings Call was also coming up at the time of the video’s posting.

Luminar had linked the video at the top of their homepage but has since taken it down. While Mark did not admit to being sponsored by Luminar, there appear to be more distinct conflicts of interest, as Mark’s charity foundation has received donations from Luminar’s CEO.

Given the positivity of the results for Luminar, it seems that the video had been well-designed and well-timed to take advantage of the current wave of negativity against Tesla, while also driving up Luminar’s stock.

Flaw 4: Vision-based Depth Estimation

The next flaw to address is the fact that humans and machines can judge depth using vision. On X, user Abdou ran the “invisible wall” through a monocular depth estimation model (DepthAnythingV2) - one that uses a single image with a single angle. This fairly simplified model can estimate the distance and depth of items inside an image - and it was able to differentiate the fake wall from its surroundings easily.

Tesla’s FSD uses a far more advanced multi-angle, multi-image tool that stitches together and creates a 3D model of the environment around it and then analyzes the result for decision-making and prediction. Tesla’s more refined and complex model would be far more able to easily detect such an obstacle - and these innovations are far more recent than the 5-year-old Autopilot stack.

While detecting distances is more difficult in a single image, once you have multiple images, such as in a video feed, you can more easily decipher between objects and determine distances by tracking the size of each pixel as the object approaches. Essentially, if all pixels are growing at a constant rate, then that means it’s a flat object — like a wall.

Case in Point: Chinese FSD Testers

To make the case stronger - some Chinese FSD testers took to the streets and put up a semi-transparent sheet - which the vehicle refused to drive through or drive near. It would immediately attempt to maneuver away each time the test was engaged - and refused to advance with a pedestrian standing in the road.

Thanks to Douyin and Aaron Li for putting this together, as it makes an excellent basic example of how FSD would handle such a situation in real life.

Flaw 5: The Follow-Up Video and Interview

Following the community backlash, Mark released a video on X, hoping to resolve the community’s concerns. However, this also backfired. It turned out Mark’s second video was of an entirely different take than the one in the original video - this was at a different speed, angle, and time of initiation.

Mark then followed up with an interview with Philip DeFranco (below), where he said that there were multiple takes and that he used Autopilot because he didn’t know that FSD could be engaged without a destination. He also answered here that Luminar supposedly did not pay him for the video - even with their big showing as the “leader in LiDAR technology” throughout the video.

Putting It All Together

Overall, Mark’s video was rather duplicitous - he recorded multiple takes to get what he needed, prevented Tesla’s software from functioning properly by intervening, and used an outdated feature set that isn’t FSD - like his video is titled.

Upcoming Videos

Several other video creators are already working to replicate what Mark “tried” to test in this video.

To get a complete picture, we need to see unedited takes, even if they’re included at the end of the video. The full vehicle specifications should also be disclosed. Additionally, the test should be conducted using Tesla’s latest hardware and software—specifically, an HW4 vehicle running FSD v13.2.8.

In Mark’s video, Autopilot was engaged just seconds before impact. However, for a proper evaluation, FSD should be activated much earlier, allowing it time to react and, if capable, stop before hitting the wall.

A wave of new videos is likely on the way—stay tuned, and we’ll be sure to cover the best ones.

Tesla Plans Massive 10x Robotaxi Expansion: A Look at the Potential New Area

By Karan Singh
Not a Tesla App

With Tesla’s first major expansion of the Robotaxi Geofence now complete and operational, they’ve been hard at work with validation in new locations - and some are quite the drive from the current Austin Geofence.

Validation fleet vehicles have been spotted operating in a wider perimeter around the city, from rural roads in the west end to the more complex area closer to the airport. Tesla mentioned during their earnings call that the Robotaxi has already completed 7,000 miles in Austin, and it will expand its area of operation to roughly 10 times what it is now. This lines up with the validation vehicles we’ve been tracking around Austin.

Based on the spread of the new sightings, the potential next geofence could cover a staggering 450 square miles - a tenfold increase from the current service area of roughly 42 square miles. You can check this out in our map below with the sightings we’re tracking.

If Tesla decides to expand into these new areas, it would represent a tenfold increase over their current geofence, matching Tesla’s statement. The new area would cover approximately 10% of the 4,500-square-mile Austin metropolitan area. If Tesla can offer Robotaxi services in that entire area, it would prove they can tackle just about any city in the United States.

From Urban Core to Rural Roads

The locations of the validation vehicles show a clear intent to move beyond the initial urban and suburban core and prepare the Robotaxi service for a much wider range of uses.

In the west, validation fleet vehicles have been spotted as far as Marble Falls - a much more rural environment that features different road types, higher speed limits, and potentially different challenges. 

In the south, Tesla has been expanding towards Kyle, which is part of the growing Austin-San Antonio suburban corridor spanning Highway 35. San Antonio is only 80 miles (roughly a 90-minute drive) away, and could easily become part of the existing Robotaxi area if Tesla obtains regulatory approval there.

In the East, we haven’t spotted any new validation vehicles. This is likely because Tesla’s validation vehicles originate from Giga Texas, which is located East of Austin. We won’t really know if Tesla is expanding in this direction until they start pushing past Giga Texas and toward Houston.

Finally, there have been some validation vehicles spotted just North of the new expanded boundaries, meaning that Tesla isn’t done in that direction either. This direction consists of the largest suburban areas of Austin, which have so far not been serviced by any form of autonomous vehicle.

Rapid Scaling

This new, widespread validation effort confirms what we already know. Tesla is pushing for an intensive period of public data gathering and system testing in a new area, right before conducting geofence expansions. The sheer scale of this new validation zone tells us that Tesla isn’t taking this slowly - the next step is going to be a great leap instead, and they essentially confirmed this during this Q&A session on the recent call. The goal is clearly to bring the entire Austin Metropolitan area into the Robotaxi Network.

While the previous expansion showed off just how Tesla can scale the network, this new phase of validation testing is a demonstration of just how fast they can validate and expand their network. The move to validate across rural, suburban, and urban areas simultaneously shows their confidence in these new Robotaxi FSD builds.

Eventually, all these improvements from Robotaxi will make their way to customer FSD builds sometime in Q3 2025, so there is a lot to look forward to.

Caught on Video: Tesla FSD Tackles a Toll Booth — Here’s How It Pulled It Off

By Karan Singh
@DirtyTesLa on X

For years, the progress of Tesla’s FSD has been measured by smoother turns, better lane centering, and more confident unprotected left turns. But as the system matures, a new, more subtle form of intelligence is emerging - one that shifts its attention to the human nuances of navigating roads. A new video posted to X shows the most recent FSD build, V13.2.9, demonstrating this in a remarkable real-world scenario.

Toll Booth Magic

In the video, a Model Y running FSD pulls up to a toll booth and smoothly comes to a stop, allowing the driver to handle payment. The car waits patiently as the driver interacts with the attendant. Then, at the precise moment the toll booth operator finishes the transaction and says “Have a great day”, the vehicle starts moving, proceeding through the booth - all without any input from the driver.

If you notice, there’s no gate here at this toll booth. This interaction all happened naturally with FSD.

How It Really Works

While the timing was perfect, the FSD wasn’t listening to the conversation for clues (maybe one day, with Grok?) The reality, as explained by Ashok Elluswamy, Tesla’s VP of AI, is even more impressive.

FSD is simply using the cameras on the side of the vehicle to watch the exchange between the driver and attendant. The neural network has been trained on enough data that it can visually recognize the conclusion of a transaction - the exchange of money or a card and the hands pulling away - and understands that this is the trigger to proceed.

The Bigger Picture

This capability is far more significant than just a simple party trick. FSD is gaining the ability to perceive and navigate a world built for humans in the most human-like fashion possible.

If FSD can learn what a completed toll transaction looks like, it’s an example of the countless other complex scenarios it’ll be able to handle in the future. This same visual understanding could be applied to navigating a fast-food drive-thru, interacting with a parking garage attendant, passing through a security checkpoint, or boarding a ferry or vehicle train — all things we thought that would come much later.

These human-focused interactions will eventually become even more useful, as FSD becomes ever more confident in responding to humans on the road, like when a police officer tells a vehicle to go a certain direction, or a construction worker flags you through a site. These are real-world events that happen every day, and it isn’t surprising to see FSD picking up on the subtleties and nuances of human interaction.

This isn’t a pre-programmed feature for a specific toll booth. It is an emergent capability of the end-to-end AI neural nets. By learning from millions of videos across billions of miles, FSD is beginning to build a true contextual understanding of the world. The best part - with a 10x context increase on its way, this understanding will grow rapidly and become far more powerful.

These small, subtle moments of intelligence are the necessary steps to a truly robust autonomous system that can handle the messy, unpredictable nature of human society.

Latest Tesla Update

Confirmed by Elon

Take a look at features that Elon Musk has said will be coming soon.

More Tesla News

Tesla Videos

Latest Tesla Update

Subscribe

Subscribe to our weekly newsletter