Rivian and Volkswagen to Create Joint Venture as VW Invests $5 Billion in Rivian -- Stock Soars

June 25, 2024

By Karan Singh

Rivian Wave

According to recent Bloomberg and Businesswire publications, Volkswagen will be investing $5 billion USD to form a joint venture with Rivian. Rivian stock has also rallied an astounding 50% in the hours since the announcement.

$5 Billion Stake

Of the $5 billion being invested, $1 billion will be invested immediately in Rivian, and the remaining $4 billion will be invested over time. That initial $1 billion will be invested in the company by the end of 2024, pending regulatory approval. The remaining $4 billion could be a fair while out, pending the formation of the joint venture in the fourth financial quarter of 2024.

The new joint venture will be “equally controlled and owned”. This is at a critical juncture for Rivian, who has recently debuted their R2 and R3 platforms, as well as bringing a much-appreciated revamp for the R1 platform.

CEO of Volkswagen, Oliver Blume:

“Our customers benefit from the targeted partnership with Rivian to create a leading technology architecture. Through our cooperation, we will bring the best solutions to our vehicles faster and at lower cost. We are also acting in the best interest of our strong brands, which will inspire with their iconic products. The partnership fits seamlessly with our existing software strategy, our products, and partnerships. We are strengthening our technology profile and our competitiveness.”

Joint Venture

Part of the joint venture will be to help Rivian lower the cost per vehicle, while Rivian helps provide Volkswagen with its new Zonal control technology, which optimizes and shortens the circuit paths in a vehicle.

The Rivian platforms are also expected to serve as the foundation for future EV vehicles from Volkswagen. Rivian will be maintaining its intellectual property rights throughout the joint venture, which will provide an ongoing revenue stream for the company, as it settles into a new and faster production rhythm.

Rivian CEO, RJ Scaringe, said:

“We’re very excited to be partnering with Volkswagen Group. Since the earliest days of Rivian, we have been focused on developing highly differentiated technology, and it’s exciting that one of the world’s largest and most respected automotive companies has recognized this. Not only is this partnership expected to bring our software and associated zonal architecture to an even broader market through Volkswagen Group’s global reach, but this partnership also is expected to help secure our capital needs for substantial growth. Rivian was created to help the world to transition away from fossil fuels through compelling products and services, and this partnership is beautifully aligned with that mission.”

Battery Venture Speculation

There is another opportunity for both companies, as Volkswagen moves to establish a 370-acre battery “giga factory” in Ontario, Canada. This could be an opportunity for Rivian and Volkswagen to continue their cooperation on battery technology, as Volkswagen intends to use the Rivian platforms for their vehicles.

Volkswagen has only recently broken ground at the site and expects the factory to produce about 90 GWh of batteries per year – or enough for roughly 1 million EVs a year. Volkswagen and the Ontario government expect that the factory should be fully functional by 2027. We could expect batteries from this Ontario factory to end up in Rivian vehicles in the future!

How Tesla Will Automate Data Labeling for FSD

February 6, 2025

By Karan Singh

Not a Tesla App

In our continued series exploring Tesla’s patents, we’re taking a look at how Tesla automates data labeling for FSD. This is Tesla patent WO2024073033A1, which outlines a system that could revolutionize how Tesla trains FSD.

We’ll be approaching this article the same way as others in the past, by breaking it down into easily digestible portions.

If you missed out on previous articles, you can dive into how FSD works or look at Tesla’s Universal Translator.

The Challenge of Data Labelling

Training a sophisticated AI model like FSD requires a tremendous amount of data. But all of that data needs to be labeled - and traditionally, this process has been done manually. Human reviewers have to go in and categorize and tag hundreds of thousands of data points across millions of hours of video.

This isn’t just laborious and rote work, it's time consuming, expensive, and prone to human error. The perfect job to hand off to AI.

Tesla’s Automated Solution

Tesla’s patent introduces a model-agnostic system for automated data labeling. Just like their previous patent on the Universal Translator, this will function for any AI model - but FSD is really what it is for.

The system works by leveraging the vast amounts of data collected by Tesla’s fleet to create a 3D model of the environment, which is then automatically used to label new data.

Three Step Process

This process has three steps, so we’ll look at each individually.

High-Precision Mapping

The system starts by creating a highly accurate 3D map of the environment. This involves fusing data from multiple Tesla vehicles equipped with cameras, radar, and other sensors. The map includes detailed information about roads, lane markings, buildings, trees, and other static objects.

It's like creating a digital twin of the real world, and this is exactly the simulation data that Tesla uses to rapidly test FSD. The system continuously improves its accuracy as it processes more data and also generates better synthetic data to augment the training dataset.

Multi-Trip Reconstruction

To refine the 3D model and capture dynamic elements of the environment, the system analyzes data from multiple trips through the same area. This allows it to identify moving objects, track their trajectories, and understand how they interact with the static environment. This way, you have a dynamic, living 3D world that also captures the ebb and flow of traffic and pedestrians.

Automated Labelling

Once the 3D model is sufficiently detailed, it becomes the key to automated labeling. When a Tesla vehicle encounters a new scene, the system compares the real-time sensor data with the existing 3D model. This allows it to automatically identify and label objects, lane markings, and other relevant features in the new data.

Benefits

There are three simple benefits to this system, which is what makes it so valuable.

It is far more efficient. Automated data labeling drastically reduces the time and resources required to prepare training data for AI models. This accelerates development cycles and allows Tesla to train its AI on much larger datasets.
It is also scalable. This system can handle massive datasets derived from millions of miles of driving data collected by Tesla's fleet. As the fleet grows and collects more data, the 3D models become even more detailed and accurate, further improving the automated labeling process.
Finally, it is accurate. By eliminating human error and bias, automated labeling improves the accuracy and consistency of the labeled data. This leads to more robust and reliable AI models. Of course, human review is still involved, but that’s only to catch and flag errors.

Applications

While this technology has significant implications for FSD, Tesla can use this automated labeling system to train AI models for various tasks.

Object detection and classification: Accurately identifying and categorizing objects in the environment, such as vehicles, pedestrians, traffic signs, and obstacles.

Kinematic analysis: Understanding the motion and behavior of objects, predicting their trajectories, and anticipating potential hazards.

Shape analysis: Recognizing the shapes and structures of objects, even when partially obscured or viewed from different angles.

Occupancy and surface detection: Creating detailed maps of the environment, identifying occupied and free space, and understanding the properties of different surfaces (e.g., road, sidewalk, grass).

These different applications are all used by Tesla - which uses different AI subnets to analyze all these different things before feeding them into the greater model that is FSD, which means things like pedestrians, lane markings, and traffic controls are all labeled on-vehicle.

In a Nutshell

Tesla's automated data labeling system is a game-changer for AI development. By leveraging the power of its fleet and 3D mapping technology, Tesla has created a self-learning system that continuously improves its ability to understand and navigate the world.

Imagine a world where self-driving cars can label and understand the world around them without human help. This patent describes a system that could make that possible. It uses data collected from many Tesla vehicles to create a 3D model of the environment, which is like a virtual copy of the real world.

This 3D model is then used to label new images and sensor data, eliminating most needs for human intervention. The system can recognize objects, lane markings, and other important features, making it easier to train AI models.

Tesla’s Giga Berlin Artwork: Where Creativity Meets Autonomy [VIDEO]

February 5, 2025

By Karan Singh

@tobilindh on X

Back in 2021, while Giga Berlin was still undergoing construction, Elon Musk said that he wanted to fill the factory with graffiti artwork. Just months later, Tesla posted a submission link to find local artists for the project.

It remained relatively quiet for about two years until Musk resurfaced with a post congratulating the team on their progress—and revealing that the factory’s concrete would be entirely covered in art. By 2023, that vision was already taking shape. Tesla began by collaborating with local artists, who created much of the artwork seen in the 2023 image above.

The Giga Berlin West Side in 2023

Not a Tesla App

Graffiti at Scale

As expected from Tesla, they didn’t just hire a group of artists to paint and scale the walls. True to their ethos of autonomy, robotics, and innovation, they sought a more futuristic approach. The local crews couldn’t work fast enough or cover enough ground, so Tesla did what it does best—push the boundaries of technology.

Covering an entire factory in art is a massive undertaking, especially when that factory spans 740 acres (1.2 sq mi / 3 km²). With such an immense canvas, Tesla needed a high-tech solution.

More of the awesome digital artwork

@tobilindh on X

Enter a graffiti start-up that had developed a robotic muralist. Tesla partnered with the company, sourcing digital artwork from independent artists while also commissioning pieces from its in-house creative team. Armed with this collection, the robot meticulously printed the artwork directly onto the factory’s concrete, turning Gigafactory Berlin-Brandenburg into a futuristic masterpiece.

The Robot

Not a Tesla App

This ingenious little robot is equipped with a precision printhead and a sophisticated lifting mechanism. It moves using two kevlar cables that allow it to glide up, down, left, and right while a pair of propellers generates downforce to keep it steady against the wall.

The printhead itself is capable of painting approximately 10 million tiny dots per wall, adding up to a staggering 300 million dots just for the west-facing side of Giga Berlin. Each mural features five distinct colors, and the robot carries 12 cans of paint, ensuring it can keep working for extended periods without interruption.

Check out the video below to see the robot action, along with mesmerizing time-lapse footage of the printing process. It’s an exciting glimpse into how Tesla is blending technology and creativity at Giga Berlin—and we can’t wait to see what’s next.