Decoding Tesla: How Full Self-Driving (FSD) Really Works

May 28, 2025.

1.78 million— that’s the number of electric cars Tesla sold in 2024, ranking second only to BYD. Tesla’s revenue for 2024 crossed $97 billion. To put this growth into perspective, Apple’s annual revenue from iPhone sales in 2024 was approximately $200 billion—roughly twice Tesla’s revenue. It’s staggering to think that a car company that’s been around for just the last 10 years has generated revenue that’s about 50% of Apple’s iPhone division—a brand widely regarded as one of the most trusted in the world.

If that’s not impressive enough, Tesla has significantly expanded its global footprint. More than 50% of its production still comes from Shanghai, but the list of countries importing Tesla vehicles is growing at lightning speed: Australia, China, Norway, Germany, Belgium, Finland, Romania, Brazil, Turkey—the list goes on. While these numbers and Tesla’s global reach demonstrate its growing significance, Elon Musk’s mission to make these cars fully autonomous is a critical and novel contributor to this growth. Building an electric car today isn’t as challenging as it once was, but making them autonomous is a completely different challenge— one that Tesla seems to have mastered over these past 8 years — and the result is Tesla’s Full Self-Driving System. 

Before we decode this system, I want to ensure readers understand that the name "Full Self-Driving" could be a misnomer.  Technically, to classify a car as fully autonomous under industry standards, such as SAE Level 5 autonomy, manufacturers must demonstrate that the vehicle does not require a steering wheel, a human driver, or any form of human supervision. This also implies that the vehicle must be designed either to handle all edge cases it might encounter over its lifetime or to have robust safeguards in place that prevent these edge cases from causing failures or harm to passengers, pedestrians, and surrounding vehicles.

In Tesla’s case, and for many vehicles claiming to be autonomous today, while they can perform certain tasks autonomously, human supervision is still required. The person in the driver’s seat remains responsible for the vehicle. With that understanding, it's now time for us to first understand the roots of Tesla and then its brain — Tesla’s Full Self-Driving System (FSD).

Evolution of Tesla 

Tesla Motors was conceptualized by engineers Martin Eberhard and Marc Tarpening in 2003 in San Carlos, California, USA. This duo was inspired to build electric cars after General Motors (GM) shut down its EV program, GM EV1, around 1999. Most of the GM EV1 vehicles ended up in museums and research centers and were the first commercially launched electric vehicles in the world.

It was in 2004 when Elon invested in Tesla and later became its chairman. Musk invested approximately $30 million, redesigned the concept platform, and launched the first version of Tesla’s EV, the Roadster, in 2006 in California. Over the next 10 years, in partnership with one of his most respected engineers, J.B. Straubel, who spearheaded Tesla’s transition to mass EV production, Tesla launched the Model S and Model X, and this is where the first milestone of mass-scale production of EVs was achieved. The story doesn’t end there. They were still not autonomous.

Birth of Tesla’s Autopilot Program 

Around 2014, Musk decided to jumpstart the next phase of his mission—making Tesla’s electric cars autonomous. This led to the birth of Tesla’s Autopilot program. Musk initially hired Chris Lattner, a former Apple executive known for creating the Swift programming language, to lead the program. Lattner ran it for about six months before stepping down, and the reins were handed back to Musk. This was when Musk brought on one of the most respected AI and ML scientists of our time, Andrej Karpathy.

Karpathy, who had previously been a founding member and research scientist at OpenAI (2011-2015), took over as Director of AI and Autopilot Vision in 2017, reporting directly to Musk. Under Karpathy’s leadership, Tesla’s self-driving program advanced by leaps and bounds in just a few years.

Among Karpathy’s most significant and novel contributions to Tesla were:

• Developing advanced neural networks that process sensor information, enabling Tesla vehicles to perceive their surroundings and make real-time decisions.

• Transitioning Tesla’s autonomous systems from expensive lidar-based technology to a vision-only approach, relying on cameras and, in some cases, complementary radar systems, making the system more affordable.

• Overseeing the large-scale training of neural networks for Tesla’s self-driving system and leading the development of the Dojo supercomputer to optimize this software.

  
  

Now that we’ve explored Tesla’s evolution and the key individuals behind this transformation, many of you are probably wondering: So, how does this system actually work, and what does it look like? Before we dive into Tesla’s self-driving system, let’s first understand how a standardized self-driving system works.

The Standard Framework for Self-Driving Systems

If you’ve read our first blog—Automotive Is Now Autonomous—you probably understand by now that a self-driving system consists of both hardware and software. The hardware includes sensors and a computer that processes the information captured from these sensors. The software, powered by AI models, comprises four key components: perception, localization, path planning, and motion control.

• Perception enables the car to see the world.

• Localization tracks the car’s position in real-time.

• Path planning crafts safe and optimal trajectories from the source to the destination.

• Motion control executes those trajectories seamlessly.

  
  

Typically, data from a fleet of test vehicles equipped with sensors is collected, fed into AI models for training, and then deployed to the computers running on the production fleet of self-driving cars. While this is a standard approach in the industry, Tesla stands out for its vision-only strategy, which relies heavily on cameras and neural networks, significantly reducing the dependency on other expensive sensors like lidar.  You might ask, well what's novel in Tesla's approach?

FSD: Tesla’s Vision-Only Approach & Architecture

The first notable distinction in Tesla’s FSD system is how it unifies the perception and localization functions into a unified module called the vision system. This vision system not only observes the environment identifying nearby vehicles, pedestrians, traffic lights, and road features, but also determines the car’s precise location within that context. The output from the vision system is then passed to the path planning module, which generates safe and efficient routes. These routes are executed using the motion control system, which activates steering, brakes, and acceleration in accordance with those trajectories.

The second notable distinction is that, from a sensor perspective, Tesla’s vision system relies solely on 8 cameras and does not use lidars. It employs a dedicated neural network architecture to process data captured by these cameras, enabling the car to perceive its surroundings, track its position, develop routes, and navigate effectively. 

Now, let’s take a closer look at the first module: the vision system.

Tesla’s Vision-Only System: 8 Cameras & Neural Networks

The 8 cameras that are the core component of Tesla's vision system are strategically placed around the vehicle to generate a coherent 360-degree view of its surroundings. However, in the initial days, the Tesla team faced a significant challenge: despite using high-definition cameras, the software system was still generating flat, 2D images. 

While the cameras provided continuous video streams, essentially a series of still images captured in rapid succession, the neural networks processing this data could only visualize the world in two dimensions. It meant these video streams and images lacked the depth that was needed to fully understand the 3D world around the car. This limitation made it extremely difficult to identify crucial 3D features essential for self-driving cars, such as elevation differences between curbs and roads, slopes, ramps, and more. Without these 3D features, enabling true self-driving capabilities becomes nearly impossible, as the real world operates in three dimensions. Overcoming this challenge became a pivotal milestone in Tesla’s pursuit of full autonomy.

To address this challenge, Tesla turned to advanced computer vision techniques powered by neural networks. These neural networks process the multi-camera feeds and fuse them into a unified 3D vector space — essentially reconstructing a precise 3D map of the vehicle’s surroundings. This enriched spatial map enables the FSD system to detect obstacles, pedestrians, and nearby vehicles with much higher accuracy, allowing for efficient and safer driving decisions, especially in complex urban environments.

At the heart of this solution lies Tesla’s Full Self-Driving (FSD) system architecture — a sophisticated, multi-layered system enabling autonomy in modern Tesla vehicles.  This system architecture is organized into eight key layers:  Raw Image Layer, Rectification Layer, RegNet Layer, BiFPN Layer, Multi-Camera Fusion & Vector Space Layer, Feature Queue Layer, Video Module (FUSE) Layer, and Hydra Neural Network Layer. Each layer plays a critical role in enabling Tesla's vehicles to perceive, interpret, and navigate their surroundings with precision. 

Now, let’s break down each layer step by step.

1. Raw Image Layer

Tesla’s FSD system starts with the raw image layer, where eight high-definition cameras are strategically placed around the vehicle to provide a 360-degree view of its surroundings. These cameras capture video streams at 1280x960 resolution with 12-bit HDR at 36 frames per second, offering a wealth of visual data. However, due to their physical placement on different parts of the car, the camera feeds naturally have slight misalignments, introducing geometric distortions in the combined 3D view of the environment. So, how does Tesla ensure this raw data is spatially aligned and accurate for further processing?

2. Rectification Layer

This is where the rectification layer plays a critical role. It calibrates and aligns the outputs from all eight cameras, compensating for differences in perspective, angle, and distortion to ensure that all image streams are geometrically aligned. Think of it like fine-tuning binoculars to create a clear, singular view. This alignment is crucial for generating reliable image data for further processing. But even with aligned images, how does the system extract meaningful insights, such as objects or road features?

3. RegNet Layer

This is where the RegNet layer comes into play.  It’s a deep neural network designed to perform hierarchical feature extraction — meaning it processes image data at multiple scales. At its lower layers, RegNet identifies simple patterns like edges, colors, and textures, while its higher layers detect complex features such as vehicles, pedestrians, traffic signs, and lane markings. This layered analysis is essential for constructing a coherent understanding of the environment.

Yet, even with such a sophisticated image processing system, one core challenge remains: determining the distance between the car and other objects in the scene. Distance estimation isn’t just about detecting an object. It depends on interpreting its accurate size in the camera feed. This is easier said than done. For example, a car located closer to the Tesla will appear larger and more pronounced in the camera feeds, while a car farther away will appear smaller and less detailed. The system must correctly interpret these varying visual scales to accurately judge how far each object is. 

So, how does Tesla’s FSD system manage to process both near and far objects with equal precision, ensuring reliable distance estimation? That's where the BiFPN layer plays a critical role.

4. BiFPN Layer

The Bi-directional Feature Propagation Network (BiFPN) layer tackles the challenge of multi-scale object detection by intelligently combining the features extracted by RegNet. It allows the system to fuse both low-level and high-level features across different image scales, ensuring that objects at varying distances — whether it’s a car 100 meters away or a pedestrian just 2 meters ahead — are accurately detected and interpreted.

You might ask how this layer works? We cover this in detail in our “Self-Driving Car Foundations” course, but for now, here is the simplified version. 

Think of BiFPN like a smart filter that passes information both downward and upward through the network layers. It strengthens the most useful visual signals and reduces the noise, making sure the system pays attention to the right details at each distance. This way, Tesla’s FSD gets a complete and balanced view — capturing both the fine details of nearby objects and the bigger picture of the environment ahead. It's this mechanism that allows Tesla to detect a car 100 meters away as easily as a pedestrian 2 meters away with precision and accurate position of each of those two objects. 

But even after addressing the challenge of detecting objects at different distances, one major hurdle remains: Tesla’s system relies on eight separate cameras, each capturing its own perspective of the world. How does the FSD system take these eight independent viewpoints and integrate them into a single, cohesive 3D understanding of the surroundings? That’s where the next layer comes into play.

5. Multi-Camera Fusion & Vector Space Layer

The multi-camera fusion layer solves this challenge by merging data from all eight cameras into a 3D vector space — effectively building a real-time, spatially accurate model of the car’s environment. This fused model allows the system to “see” its surroundings as a singular 3D space, rather than as fragmented snapshots captured from eight separate cameras. It’s this 3D fusion that gives Tesla’s FSD system the spatial awareness needed to understand where the other agents in the scene are, their position, and how each of them relate to the car’s own position.

However, even with this rich 3D map, one critical piece is still missing: understanding how other agents in the scene move over time. After all, safe driving isn’t just about knowing where all the agents, such as pedestrians, other cars, or cyclists are in the scene at the moment; it’s about anticipating where they’ll head next. The system needs to recognize these patterns and adjust its plans accordingly. 

So, how does Tesla’s FSD incorporate this crucial temporal understanding into its decision-making system?

6. Feature Queue Layer

The Feature Queue Layer introduces a crucial temporal dimension to Tesla’s FSD system by storing and tracking feature data over time. Instead of treating each camera frame as an isolated snapshot, this layer creates a rolling memory caching past observations, so the system can understand how objects are moving, estimate their speed, and predict their future trajectory.  

For example, it doesn’t just detect where a nearby car is right now; it can forecast where that car will likely be a few seconds from now allowing the system to anticipate rather than merely react. This proactive awareness is essential for adaptive navigation, especially in dynamic environments, like busy intersections or highways where multiple agents are interacting simultaneously. 

Tracking individual objects is only part of the puzzle. To enable full autonomous driving, the system must interpret the entire environment holistically — integrating the motion of all agents (cars, pedestrians, cyclists) into a unified model. Let’s take a look into how does Tesla’s FSD system ensure it has a holistic understanding of all agents in the scene.

7. Video Module (FUSE) Layer

The video module addresses this challenge by combining two essential streams of information: (1.) spatial data — where objects like cars, pedestrians, cyclists, and traffic lights are positioned at the moment and time, and (2.) temporal data — how those objects have been moving over time, as tracked by the Feature Queue.  

The Feature Queue acts like a rolling memory. It stores sequences of past observations, letting the system track how objects have moved over time, rather than treating each image as a static snapshot. Tesla strengthens this temporal insight through its future queue, which goes a step further by anticipating where each object in the scene is heading next. Together, these inputs feed into advanced neural networks that build a dynamic, predictive model of the road ahead. 

While the FUSE Layer gives the system a unified, predictive understanding of the scene tracking where objects are now and where they’re going, it doesn’t directly handle the specialized tasks required to enable autonomous driving. In other words, up to this point, the system has built a detailed 3D predictive model of the world, but it hasn’t yet translated that into the concrete decisions needed to actually drive the car. There’s a wide range of decisions the system must make in real time. This is where the Hydra Neural Network comes in.

8. Hydra Neural Network Layer

The Hydra Neural Network Layer enables a vehicle to translate 3D insights developed in layers 1–7 into decisions that power autonomous driving system. While the layers 1-7 focus on building a unified model of the environment, Hydra branches off into multiple specialized outputs — each designed to address a distinct function essential for autonomous driving. These include tasks like precise object detection, traffic light state recognition, lane boundary identification, road sign classification, and the estimation of surrounding vehicles’ speeds and trajectories.

By combining these specialized outputs, the Hydra layer transforms the shared 3D model into a detailed, actionable representation of the driving scene — one that not only predicts how the environment will evolve, but also isolates the critical signals needed for safe and effective navigation. Even with this robust perception and localization system, however, how does Tesla translate these predictions into actionable driving decisions?

Path Planning and Motion Control

This is where Tesla’s Vision system feeds data into the path planning module. With a clear 3D view and predictions of object trajectories, the path planning module calculates the safest and most efficient route to destination. The motion control system then takes over, translating this route into precise steering, acceleration, and braking commands, ensuring the vehicle follows the planned path in real time.

However, with so many moving parts, how does Tesla ensure its system can handle dynamic and unpredictable driving scenarios? We cover the system in great detail in our course on Self-driving Cars here at Boring Sage. This course focuses on what truly matters: understanding the core concepts, learning how companies like Tesla, Waymo, and Zoox build their systems, and gaining hands-on experience through real-world projects. You don’t need a formal degree—just the right knowledge and practical skills. That's what we offer in this course. 

Built on years of expertise and a deep passion for these emerging fields, it’s designed to make your learning journey both engaging and rewarding. If you enjoyed this read, you’ll find this course incredibly valuable.

Cheers,

Prathamesh 

Disclaimer: This blog is for educational purposes only and does not constitute financial, business, or legal advice. The experiences shared are based on past events. All opinions expressed are those of the author and do not represent the views of any mentioned companies. Readers are solely responsible for conducting their own due diligence and should seek professional legal or financial advice tailored to their specific circumstances. The author and publisher make no representations or warranties regarding the accuracy of the content and expressly disclaim any liability for decisions made or actions taken based on this blog.