As a continuation of our series on Tesla’s patents, we’re taking a look at how Tesla optimizes the performance of AI - FSD, in this case - in autonomous vehicles and robots. Patent WO2024073115A1 goes over efficiently running complex AI models on specialized hardware.
Before we dive into this article, we recommend reading our article on How FSD Works and our other article on Tesla’s Universal Translator for streamlining FSD deployments. While they’re not necessary, the background knowledge will help you appreciate all the details behind how Tesla does their optimization.
Just like before, we’ll be breaking this down into sections and making them as easily understandable as possible.
AI Subnetworks
FSD isn’t a monolithic entity - it is composed of smaller, specialized sub-networks, each dedicated to a specific aspect or function of autonomous operation. This modular design means that Tesla can work on improving one or all sections through training. When one section is improved, the end-to-end nature of the AI also means that the other sections will learn to adapt to the improvements and, therefore, perform better. It also allows for more efficient processing and adaptability during deployment and initial platform training.
These sub-networks might be responsible for tasks such as:
Recognizing and interpreting traffic signals
Detecting and tracking moving objects including vehicles, pedestrians, cyclists, and more
Maintaining lane position and navigating roads
Generating 3D maps of the surrounding environment
Planning paths and making real-time driving decisions
This division of labor allows FSD to handle the complexities of autonomous driving with greater efficiency and precision
Tailored Compilers
Different hardware components are good at different things - and they also require different types of instructions. CPUs, GPUs, and specialized AI accelerators (NPUs) all have unique architecture and capabilities.
Tesla uses a compiler toolchain to translate FSD into machine code that is specifically tailored to each hardware component. This ensures that instructions are executed optimally on each processor, maximizing performance and efficiency.
Strategic Assignment
To further optimize performance, Tesla employs a system that intelligently assigns each FSD sub-network to the most suitable hardware component. This ensures that computationally demanding tasks are handled by the most powerful processors while simpler tasks are delegated to more efficient units.
This strategic assignment of tasks maximizes the overall efficiency of the system, ensuring that each component operates within its optimal performance range.
Optimized Scheduling
The order in which the hardware executes instructions also plays a crucial role in performance. Tesla's system includes an "execution scheduler" that determines the most efficient sequence of operations, minimizing delays and maximizing real-time responsiveness.
This optimized scheduling ensures that the FSD can react quickly and make informed decisions in dynamic driving situations - or quick-response situations with Optimus - like catching a ball.
While the demo here has been confirmed to be teleoperated, Tesla has said they’re working to let Optimus do this autonomously in the future.
To reduce the computational burden and power consumption of FSD, Tesla employs a technique called "quantization-aware training." This involves training FSD to work with lower-precision numbers, which require less processing power and memory. Essentially - rounding.
This approach allows the AI to operate efficiently without significantly compromising accuracy, striking a balance between performance and resource utilization.
Clock Synchronization
In hardware systems with multiple chips, maintaining precise timing is crucial for accurate and synchronized operation. Tesla's system incorporates mechanisms to synchronize the clocks of all processing units, preventing timing errors and ensuring seamless coordination between different components.
This precise clock synchronization is essential for FSD to make accurate real-time calculations and respond effectively to changing conditions.
Redundancy and Failover
To ensure reliability and safety, Tesla's system supports redundant hardware configurations. This means that if a critical component fails, a backup component can seamlessly take over, preventing disruptions in operation.
This redundancy and failover capability is crucial for maintaining the safety and integrity of autonomous systems, especially when driving. Tesla has built-in both physical and software redundancy to FSD, ensuring that it maintains a minimum standard of safety when operating autonomously.
In Simpler Terms…
Imagine a large company (FSD) with different departments (sub-networks) responsible for specific tasks. Each department has its own specialized tools and equipment (hardware components). Tesla's system acts like an efficient management structure, assigning the right tasks to the right departments, providing them with the appropriate tools, and coordinating their efforts for optimal productivity and performance.
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To show off its scalability, Tesla has officially launched its first major expansion of its Robotaxi service area in Austin, Texas. The expansion comes just 22 days after the program’s initial public launch.
That’s a stunningly quick pace that sets a benchmark for how fast we’ll be expecting Tesla to roll out additional expansions as they validate and safety-check in additional area and cities. The new geofence not only adds a significant amount of new territory, but also makes Tesla’s service area in Austin approximately 4 miles larger than Waymo’s.
The expansion, which went live for users in the early access program earlier today, reshapes the map into… what we can call an upside-down T. It helps connect more parts of the city, and increases the service area by more than double.
So far, the initial launch has been operating without any significant issues, which means Tesla is ready and willing to continue expanding the program.
Rapid Scaling
While the larger map is a clear win for early-access users and especially those who live in Austin, the most significant aspect here is just how fast Tesla is going. Achieving a major expansion in just over three weeks since its initial launch is a testament to Tesla’s generalized autonomy approach with vision only.
Unlike methods that require intensive, street-by-street HD mapping that can take months or even years just to expand to a few new streets, Tesla’s strategy is built for this type of speed.
This is Tesla’s key advantage - it can leverage its massive fleet and AI to build a generalized, easily-applicable understanding of the world. Expanding to a new area becomes less about building a brand-new, high-definition map of every street light and obstacle, but instead a targeted safety validation process.
Tesla can deploy a fleet of validation vehicles to intensely focus on one zone, allowing the neural nets to learn the quirks of that area’s intersections and traffic flows. Once a high level of safety and reliability is demonstrated, Tesla can simply just redraw the geofence.
Geofence Size
Tesla went from approximately 19.7 sq mi (51 sq km) to 42.07 sq mi (109 sq km)in just 22 days, following the initial launch and safety validation. Within a few short days of launch, we began seeing the first Tesla engineering validation vehicles, hitting Austin’s downtown core, preparing for the next phase.
The larger footprint means more utility for riders, and that’s big, especially since the new service area is approximately four square miles larger than Waymo’s established operational zone in the city.
Highways and Fleet Size
The new territory enables longer and more practical trips, with the longest trip at tip-to-tip taking about 42 minutes from the southern edge of the old geofence to the northern edge of the new geofence. For now, Tesla has limited its fleet to operating exclusively on surface streets and does not use highways to complete its routes.
We also don’t know if Tesla has increased the vehicle fleet size quite yet - but if they’re intending to maintain or reduce wait times for even the early-access riders, the fleet size will easily need to be doubled to keep up with the new area.
This video clip shows the @robotaxi follows the Interstate (I-35) but does not take the highest.
Perhaps the most telling bit about how fast Tesla is expanding is that they’re already laying the groundwork for the next expansion. Validation vehicles have been spotted operating in Kyle, Texas, approximately 20 miles south of the geofence’s southern border.
Robotaxi Validation vehicles operating in Kyle, Texas.
Financial_Weight_989 on Reddit
This means that while one expansion is being rolled out to the public, Tesla is already having its engineering and validation teams work on the next expansion. That relentless pace means that if this keeps up, Tesla will likely have a good portion of the Austin metropolitan area - the zone they’ve applied for their Autonomy license for - serviceable by the end of 2025.
The pilot? A success. The first expansion? Done. The second expansion? Already in progress. Robotaxi is going to go places, and the next question won't be about whether the network is going to grow. Instead, the new questions are: How fast, and where next?
One of the most welcome features of the recently refreshed 2026 Model S and Model X is the addition of a front bumper camera. Now, thanks to some clever work by the Tesla community, it has been confirmed that this highly requested feature can be retrofitted onto older HW4-equipped (AI4) Model S and Model X vehicles.
The discovery and first installation were performed by Yaro on a Model X, and Tesla hacker Green helped provide some additional insight on the software side.
Unused Port and a Software Switch
The foundation for this retrofit has been in place for a long time, laid by Tesla itself. All HW4-equipped Model S and Model X vehicles, even those built before the recent refresh, have an empty, unused camera connector slot on the FSD computer, seemingly waiting for this exact purpose.
While the physical port is there, getting the car to recognize the camera requires a software change. According to Green, a simple configuration flag change is all that is needed to enable the front camera view on the vehicle’s main display once the hardware is connected and ready.
The Hardware: Parts & Costs
Yaro, who performed the installation on a Model X, provided a detailed breakdown of the parts and approximate costs involved.
Front Camera - $200 USD
Bumper Grill (with camera cutout) - $80 USD
Bumper Harness - $130 USD
Washer Pump - $15 USD
Washer Hoses - $30 USD
The total cost for the Model X hardware comes to around $455 USD, which isn’t too expensive if you were to DIY it. Tesla’s Electronic Parts Catalog has some of these parts available for order, and some can be ordered via your local Service Center. Yaro did note that he had to jerry-rig the camera connector cable, having salvaged the cable from a different camera harness.
The Model S vs Model X
This is where the project varies significantly. For the Model X, the retrofit is relatively simple. Because the main bumper shape is the same, only the lower bumper grill needs to be swapped for the version with the camera opening, along with installing the camera itself and the washer hardware.
For the Model S, the process is a bit more complex and expensive. Due to the different shape of the pre-refresh bumper, the entire front fascia assembly must be replaced to accommodate the camera. This makes the project far more expensive and laborious.
DIY or Official Retrofit?
The official front bumper camera on the Model X
Not a Tesla App
Right now, this is only a DIY retrofit. Tesla hasn’t indicated that they intend to offer this as an official retrofit for older vehicles at this time, but given the fact that it isn’t too complex, we expect that there is a possibility that they may do so in the near future.
All in all, this is about 3-5 hours of labor for the Model X, and approximately 5-7 hours of labor for the Model S, based on the official Tesla Service Manuals, using the front fascia reinstall process as a guide.
That means if Tesla does offer this as a retrofit service, it will likely cost between $800 and $1,200 USD when factoring in Tesla’s labor rates, but the total cost will vary regionally.
For those who own an AI4 Model S or Model X, it could be possible to request service for this installation, but as far as we’re aware, there is no official service notice for this retrofit at this time.
What About the Model 3?
For owners of the refreshed Highland Model 3, the only vehicle now left without a front bumper camera, the possibility of a retrofit is still uncertain. It has been noted by Green that some, but not all Model 3s built in late 2024 have an empty camera port on the FSD computer. This inconsistency means that while a retrofit may be possible for a subset of Model 3s, it isn’t a guaranteed upgrade path like it is for the Model S or Model X.
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