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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In the latest episode of Jay Leno’s Garage, Tesla’s VP of Vehicle Engineering, Lars Moravy, confirmed that the new Model Y will feature adaptive headlights.
As Moravy was talking about the updated headlights in the vehicle, which now sit a few inches lower than before, he stated that in a couple of months, Tesla will add adaptive headlights in the U.S.
While Tesla has already introduced adaptive headlights in Europe and the Indo-Pacific, the feature has yet to make its way to North America.
Originally delayed in the U.S. due to regulatory issues, manufacturers have been able to implement adaptive headlights since mid-2024. Meanwhile, competitors like Rivian and Mercedes-Benz have already rolled out their own full matrix headlight systems, matching what’s available in other regions.
Update: This article has been updated to clarify that adaptive headlights will indeed be launched in the U.S., shortly after the vehicle launching in March.
Currently, Tesla in North America supports adaptive high beams and automatic headlight adjustment for curves, but full matrix functionality has yet to be rolled out. Meanwhile, matrix headlights are already available in Europe, where they selectively dim individual beam pixels to reduce glare for oncoming traffic and adapt to curves in the road.
It was surprising that matrix functionality wasn’t included in the comprehensive 2024 Tesla Holiday Update. This feature would likely improve safety ratings, so we can only assume Tesla is diligently working to secure regulatory approval.
Adaptive Headlights on Other Models
Lars didn’t confirm whether the refreshed Model Y comes with the same headlights as the new Model 3 and the Cybertruck, instead simply calling them "matrix-style” headlights.
The headlights on the new Model Y appear very similar to those available in the 2024+ Model 3, possibly meaning these other models will also receive adaptive headlight capabilities in the next couple of months.
For vehicles with older-style matrix headlights, it’s unlikely that adaptive beams support will launch at the same time, but they will hopefully become available soon afterward.
For the first time since launching Tesla Insurance in 2019, Tesla will begin underwriting its own policies, starting in California.
Tesla Insurance originally debuted in California and has since expanded to several U.S. states. Until now, policies were underwritten by State National, a subsidiary of the Markel Insurance Group. However, Tesla is now transitioning to fully in-house underwriting, beginning with its home state.
As part of this shift, California Tesla Insurance customers who receive an in-app offer to switch will be eligible for a one-time 3% discount on their next term’s premium—covered entirely by Tesla Insurance.
What is Underwriting
Underwriting is the process an insurance company uses to assess risk and determine whether to offer coverage, at what price, and under what terms.
Insurers evaluate factors such as driving history, credit score, age, vehicle type, and location. In Tesla’s case, vehicle driving data (not available in California) also plays a key role in risk assessment. These factors help classify drivers into risk categories, which influence their base premium.
From there, coverage limits, deductibles, and policy inclusions or exclusions can further adjust the final premium up or down.
Robotaxi and Other Benefits
At first glance, underwriting insurance might seem like a complex and costly process for Tesla. However, there are several compelling reasons why this move makes sense.
Insurance Income: Insurance is a highly profitable industry. Companies set rates based on risk, offering lower premiums to safer drivers and higher rates to riskier ones. This not only maximizes profitability but also incentivizes safer driving behavior, reducing overall claims.
Data Advantage: Tesla collects vast amounts of driving data through its Safety Score system. While California doesn’t allow Safety Score to impact premiums, Tesla can still use this data in the underwriting process to refine risk assessments and pricing for its vehicles.
Control Over Repair Costs: By underwriting its own policies, Tesla gains direct control over repairs and total loss decisions. This allows them to dictate when, where, and how repairs are done, optimizing costs for parts, labor, and service while ensuring vehicles are fixed according to Tesla’s standards.
FSD-Driven Discounts: Tesla has already begun offering insurance discounts for drivers using Full Self-Driving (FSD). By underwriting its own policies, Tesla could expand these incentives, potentially offering greater discounts to frequent FSD users in the future.
Preparing for Robotaxi: Perhaps the biggest long-term reason for this shift is the June launch of the Robotaxi fleet. How will Tesla insure these vehicles? The answer is simple—by underwriting its own policies and assuming liability.
Tesla’s decision to underwrite its own insurance isn’t just about cutting out middlemen—it’s a step toward lowering costs, increasing profitability, and preparing for the future of autonomous driving, a risk many insurance companies may be unwilling to make.
Further Expansion
This could be a strong sign that Tesla is preparing to expand its insurance offerings now that it has taken on the underwriting process itself. In July 2024, Tesla hired a former GEICO insurance executive to lead the expansion of Tesla Insurance and help reduce costs—a move that now appears to be paying off.
Rather than a traditional expansion, Tesla has instead made a bold move by bringing underwriting in-house, something few expected. However, it aligns with Tesla’s strategy of vertically integrating and controlling key aspects of its business, whether in manufacturing, software, or now, insurance.
If this pilot program proves successful, it could pave the way for Tesla Insurance to launch in more states—and potentially even other countries. With 2025 shaping up to be a pivotal year, we may see Tesla accelerate its insurance expansion sooner than expected.
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