Tesla builds real-time 3D worlds to train self-driving cars

Tesla has built new AI software that can turn footage from its vehicles into live, 3D drivable environments. Each Tesla uses eight cameras to record the area around it. Engineers can now use this footage to recreate the road in a virtual world and test how the Full Self-Driving (FSD) system reacts in real situations.

The technology connects video feeds from all eight cameras into one detailed 3D map. Engineers can then drive through it inside a simulator. This lets them review how the FSD behaves in complex conditions such as busy intersections, rain, or construction zones.

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Tesla has developed a way to generate a real-time 3D drivable environments using footage from all 8 cameras on its vehicles. This lets Tesla engineers virtually “drive” inside a fully simulated version of the real world with the goal of improving FSD.

It's like a video game lol https://t.co/LntQAmmsF7 pic.twitter.com/FBFc7EK4I7
— Sawyer Merritt (@SawyerMerritt) October 23, 2025

The process uses neural networks to rebuild the real world frame by frame. It recognizes lines, vehicles, signs, and people. The AI makes these virtual scenes accurate enough that engineers can replay real drives or create new ones from scratch.

Video: Real-time 3D drivable environments

A new 30-minute presentation from @aelluswamy, Tesla’s VP of AI, has been released, where he talks about FSD, AI and the team’s latest progress.

Highlight from the presentation:
• Tesla's vehicle fleet can provide 500 years of driving data every single day.

Curse of… pic.twitter.com/PAUAG01TQc
— Sawyer Merritt (@SawyerMerritt) October 23, 2025

Elon Musk said earlier that Tesla’s video system can “predict extremely accurate physics,” which helps the AI learn at scale without real-world risk.

Built for safer and faster testing

The tool gives Tesla a way to test software updates safely before releasing them. It lets engineers recreate dangerous situations that might rarely happen in real traffic. For example, they can drop in a running pedestrian, add fog, or trigger a sudden lane change to see how the car responds.

They can also replay moments when FSD made an error. The model can then show how it would handle it after retraining. This loop helps Tesla test fixes faster and fine-tune decisions that depend on complex visual data.

The AI behind it

The system runs on neural networks trained on data from millions of miles driven by Tesla cars. These networks use methods similar to Neural Radiance Fields (NeRF), which convert 2D images into realistic 3D scenes. The software processes all camera feeds to build a “vector space” view of the environment with accurate depth and motion perception.

It works in real time, thanks to Tesla’s hardware updates and cloud processing setup. The company uses powerful GPU clusters to handle the massive amount of data required for lifelike video generation.

Training at scale

Tesla’s entire fleet serves as a sensor network. Every car sends data that helps the AI learn new driving patterns. This creates what the company calls a “neural world simulator.” It combines real trips from customers with synthetic ones created by the system. This gives engineers endless driving data without logging more physical miles.

Tesla’s datasets now exceed billions of miles per year. That scale lets the AI cover more edge cases, such as rural roads or rare obstacles. Musk said these synthetic environments provide “superhuman practice” opportunities for FSD’s neural models.

Unlike Waymo or Cruise, Tesla trains its system using only cameras and neural networks, skipping LiDAR or detailed pre-mapped routes. This gives it more flexibility to handle unfamiliar areas. It can adjust to new streets or weather based purely on live visual input from vehicles.

Tesla’s integration of sensors, software, and in-house AI training makes it stand out. The fleet data gives the company an unmatched foundation to train simulation models that are both realistic and scalable.

Technical and practical limits

Real-time simulations demand high computing power, and training can take days or even weeks. Tesla previously used its Dojo supercomputer for AI training but later moved most workloads to GPU clusters managed in the cloud. Handling so much visual input requires efficient memory systems and lossless compression pipelines.

Another challenge is validating that synthetic tests match real-world physics. Any gap between the simulated and actual environment can mislead the FSD model. Tesla engineers have developed tools to evaluate that balance during replay sessions.

Tesla is using this system to train the next versions of FSD, including version 14. The company plans to expand supervised FSD use to international markets in 2025, where local conditions like traffic rules and signage differ. The 3D simulation system will make adaptation faster without field-testing in every market.

This technology may also support future products like the Optimus robot. Tesla’s AI teams already use similar modeling for training robotic movement and factory automation scenarios.

This 3D generation system is another step toward fully autonomous driving. It cuts real-world risks, lowers training costs, and reduces development time by turning every bit of road data into a usable simulation.

As Musk’s team continues refining it, Tesla gains a strong advantage in global FSD testing and in future robotics applications. If it scales as planned, this method could define how future self-driving cars learn and improve using both real and simulated worlds.

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