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Tesla Autopilot veterans launch company to accelerate self-driving development
After working on Tesla’s Autopilot team for 2.5 years, Andrew Kouri and Erik Reed decided to start their own self-driving, AI-based company rightfully named lvl5. Together with iRobot engineer George Tal, lvl5 aims to develop advanced vision software and HD maps for self-driving cars.
Founded in 2016, lvl5 was incubated at renown Silicon Valley incubator Y Combinator and later raised $2 million in seed funding from investor Paul Buchheit, who’s a partner at Y Combinator and creator of Gmail, and Max Altman’s 9Point Ventures.
In just 3 months, lvl5 racked up almost 500,000 miles of US roadway coverage with Payver. (Photo: lvl5)
“Working with lvl5’s founders while they were at Y Combinator, it was clear they have unmatched expertise in computer vision, which is the secret sauce of their solution,” said Buchheit. “I have no doubt this is the team to make self-driving a reality in the near term.”
At the center of lvl5’s technology is their computer vision algorithms. Founder and CTO George Tall previously specialized in computer vision technology at iRobot. In addition to Tall’s experience at iRobot, Kouri and Reed’s experience at Tesla undoubtedly left them with unparalleled expertise in computer vision.
Instead of turning to expensive LiDAR technology, lvl5’s computer vision analyzes its environment for stoplights, signs, potholes, and other objects. The system can be accurate to 10cm, a notable measure considering it’s derived from simple cameras and smartphones. In comparison, LiDAR systems can cost over $80,000 but are accurate to 3cm.
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- Each purple trace through the intersection contributes to building the 3D map from a 2D image. For each frame, lvl5’s computer vision technology computes the position of the vehicle relative to other objects in the intersection and create a point cloud that resembles the output from LiDAR. Each white sideways “pyramid” represents the location of a captured frame in the video trace. (Photo: lvl5)
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- This image is taken from one of lvl5’s neural nets, which is designed to draw a box around the position of traffic lights in an image. (Photo: lvl5)
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- With only two trips through this intersection, lvl5 can start to extract semantic features such as a stop sign. (Photo: lvl5)
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- The three founders of lvl5 in front of their SF home. Left to right: Erik Reed, Andrew Kouri, George Tall (Photo: Lvl5)
So how will lvl5 map roadways in the world using their computer vision technology? Smartphones. Well, for now at least. The company has released an app called Payver that allows anyone’s smartphone to collect data while driving and get paid between $.01-$.05 per mile, depending on a number of factors. Users of the app place their phone in a mount on their dashboard and let the app gather driving data.
The data is sent to lvl5’s central hub and processed by their computer vision technology. “Lvl5 is solving one of the biggest obstacles to widespread availability of self-driving technology,” said Max Altman, one of lvl5’s seed round investors and partner at 9Point Ventures. “Without accurate and efficient HD mapping, as well as the computer vision software that enables it, self-driving vehicles will take much longer to reach mass-market. This will delay everything from safer roads to efficient delivery services.”
GIF: lvl5
“We have to make self-driving available worldwide – not just in California,” Co-Founder and CEO Andrew Kouri said in a company statement. “Our approach, which combines computer vision software, crowdsourcing and widely available, affordable hardware, means our technology is accessible and will make self-driving a reality today, rather than five years from now.”
The company has already established pilot programs with major automakers and both Uber and Lyft. Companies will pay lvl5 an initial fee to use the maps, along with a monthly subscription to keep the maps continuously updated. “Through its OEM-agnostic approach, lvl5 will be able to collect significant amounts of mapping data from millions of cars in order to scale the technology for the benefit of drivers and pedestrians around the world,” the company’s press release states.
Tesla has introduced an enhanced visualization in its Supercharger navigation system, building directly on the Site Maps feature rolled out a few months ago.
This latest software update adds detailed 3D icons that represent specific vehicle models parked at charging stalls, offering drivers a more precise view of site occupancy and layout.
The Site Maps debuted in Tesla’s 2025 Holiday Update, providing 3D overviews of select Supercharger locations with real-time stall availability.
Tesla supplements Holiday Update by sneaking in new Full Self-Driving version
Drivers could see which spots were open, occupied, or out of service when navigating to supported stations.
Now, the system takes this capability further by rendering accurate representations of Tesla vehicles, including distinctions between models such as the Model 3, Model Y, Model S, Model X, and Cybertruck. These icons appear as lifelike 3D renderings, complete with recognizable shapes and proportions that match the actual cars charging at the site:
Supercharger update now shows type of Tesla at charger as well.
Pretty cool. pic.twitter.com/J3NRSIgM0m
— DennisCW | wen my L (@DennisCW_) June 2, 2026
This refinement improves the user experience during road trips and daily charging stops. As drivers approach a Supercharger, the navigation display now shows not just generic occupied markers but identifiable vehicle types plugged into each stall.
Blue indicators highlight active charging sessions, while other visual cues denote availability or maintenance status. The feature integrates seamlessly with the existing map interface, allowing quick assessment of the best available spot based on vehicle size and positioning.
Tesla continues to expand the availability of these detailed Site Maps across its global network. Initially piloted at a limited number of locations, the rollout has progressed steadily, with more stations gaining support in recent software versions.
Owners benefit from better planning, as the system helps identify compatible stalls and reduces uncertainty upon arrival. The update reflects Tesla’s ongoing commitment to refining its navigation and charging ecosystem through iterative software improvements.
In addition to model-specific icons, the enhanced maps maintain all prior functionalities, such as integration with nearby amenities and energy usage predictions. This ensures a comprehensive tool for efficient Supercharging.
As Tesla’s fleet grows and the network scales, such features play a key role in optimizing the overall ownership experience. Future updates may extend similar visualizations to additional sites and incorporate even more data points for drivers.
With this piggyback enhancement, Tesla demonstrates how small but thoughtful additions can elevate an already useful tool, making Supercharger visits smoother and more informed for its customers. The company is expected to broaden the feature’s reach in upcoming releases, further solidifying its leadership in EV charging infrastructure.
Tesla Full Self-Driving v14.3.3 driver monitoring was reportedly scaled back in recent releases, but a new version that was released in the early hours of June 3 aimed to do a better job of keeping those in control of their cars honest, according to release notes.
The release notes for FSD v14.3.3, via Software Version 2026.14.6.7 added:
“Improved driver monitoring system sensitivity with better eye gaze tracking, eye wear handling, and higher accuracy in variable lighting conditions.”
However, Tesla said this was already enabled in the first rollout of FSD v14.3.3 in late May. We tested it anyway, especially as the Standard Speed Profile seemed less-than-worried about what you were doing during operation.
I decided to try out the Hurry and Mad Max Speed Profiles for this test, and it gave me results that I would have expected. Tesla has evidently ramped up driver monitoring based on the Speed Profile you are using to travel.
The more aggressive the Speed Profile, the more on the hook you will be for taking your attention away from the road. Our testing showed that Mad Max was less likely to allow you to do normal things like change music or adjust navigation without getting an on-screen warning or nag from the driver monitoring system.
Hurry Mode Results
On Hurry, the driver monitoring system on FSD v14.3.3, via Software Version 2026.14.6.7, was more restrictive than Standard but less restrictive than Mad Max. I found that I could scroll through music options for a considerable amount of time, more than 30 seconds:
Roughly :31 between first touching the center screen and getting the first nag
— TESLARATI (@Teslarati) June 3, 2026
Standard gave me about 80 seconds of phone scrolling with absolutely no nags or warnings in a previous test. It is worth noting that this was a previous branch of v14.3.3, but Standard is such a goodie-two-shoes on the road that it is my impression it would not change much.
Here’s an 80-second phone nag test on Tesla FSD v14.3.3.
No alerts, no nagging, no annoyance. https://t.co/1dxvTOw5Cn pic.twitter.com/vYViFpjfoK
— TESLARATI (@Teslarati) May 29, 2026
Mad Max Results
I spent the majority of the drive on Mad Max to see how it truly reacted to the driver having their attention elsewhere. While I did do a short phone test, I am aiming to steer away from those and use the center screen. I think it is a valid criticism that the phone test is dangerous and, not to mention, illegal in Pennsylvania. Changing the navigation and music is a more reasonable, more responsible, and safer test.
With Mad Max being the fastest and most aggressive Speed Profile, I anticipated this being the quickest mode to give me an alert that I needed to look at the road. That was the case with music:
🎥 Testing Tesla FSD v14.3.3 (via 2026.14.6.7) nags on Mad Max https://t.co/qZALU2OujY pic.twitter.com/XddOJ0D47x
— TESLARATI (@Teslarati) June 3, 2026
As well as adjusting Navigation, when I received two nags:
🎥 Testing Tesla FSD v14.3.3 (via 2026.14.6.7) nag while adjusting navigation
Two nags here https://t.co/qZALU2OujY pic.twitter.com/xa3dtaDG1L
— TESLARATI (@Teslarati) June 3, 2026
These nags were more than reasonable, and I think it’s probably good that Tesla is ramping up the driver monitoring. I do believe that it should be relatively strict across all of the Speed Profiles, especially with phone use. When using the center screen, the nag intervals should be based on the speed profile you are utilizing at the time.
These driver monitoring adjustments are a great thing to have while FSD is still under its “Supervised” moniker, but I expect Tesla to continue pushing the limits on what it will allow, especially considering CEO Elon Musk has hinted that phone use is capable with the more recent versions.
You can watch the full drive on YouTube below:
Tesla has responded to the skeptics of its Robotaxi program by launching a massive expansion of the unsupervised program in its initial rollout city of Austin.
The company’s geofence, the enabled area of operation for rides, now covers the entire Austin Metropolitan area, an incredible move just days after media headlines attempted to discredit the ride-hailing service.
Those who have access to the Tesla Robotaxi app on their smartphones can now request a ride in any portion of the Austin Metro area. The company confirmed this on the social media platform X:
Unsupervised Robotaxi now in the entire Austin Metro area https://t.co/eXNBdarvVS
— Tesla Robotaxi (@robotaxi) June 3, 2026
This is Tesla’s fifth expansion of the geofence, with the others occurring in July, early August, late August, and late October 2025. It has remained at that size since October 26, but Tesla has now more than doubled that size.
It is now covering the entire area, including suburbs like Pflugerville and Manor, as well as I-35 highways, Gigafactory Texas, and the Austin-Bergstrom Airport.
The move comes just days after various media outlets highlighted the small fleet size of Tesla’s Robotaxi fleet in Austin, something that is a reasonable criticism but an understandable move on the company’s part to prioritize safety.
Tesla has expanded its Robotaxi geofence many times, but its fleet has remained at a relatively conservative size as the company continues to push safety as its most crucial metric.
The latest expansion is a key indicator of Tesla’s comfort level to expand the ride-hailing service. The move shows Tesla is scaling unsupervised autonomy, as it demonstrates that the company’s Full Self-Driving system has reached sufficient reliability for a broader real-world deployment, which is something the company has worked on extensively.
It also shows Tesla is game for a competition with its rivals in the autonomous ride-hailing sector. Tesla has often matched or exceeded competitors like Waymo in coverage area, despite its smaller fleet. This step highlights Tesla’s iterative, data-driven progress toward a high-margin, app-based Robotaxi network.
It’s not the absolute largest area expansion ever, but achieving full unsupervised operations across a major metro is a key moment in the Robotaxi story. It shifts the program from limited pilot/testing toward a more mature commercial service, while gathering the miles needed for faster growth.
Tesla piggybacks recent Supercharger feature with update that takes it further
Tesla Full Self-Driving v14.3.3 driver monitoring: We tested it
