Electric vehicle manufacturer Rivian has acquired Third Dimension, a U.K.-based startup founded by veterans of the gaming and autonomous vehicle industries that uses artificial intelligence to transform images and videos into lifelike three-dimensional worlds. While Rivian is not venturing into the gaming industry, the automaker aims to leverage Third Dimension’s advanced technology to turn real-world data into highly accurate 3D simulations. This capability is expected to significantly accelerate the development of Rivian’s advanced driver-assistance features and the proprietary self-driving system powering the robotaxis it plans to supply to Uber.
Financial terms of the transaction were not publicly disclosed. However, the acquisition brings Third Dimension’s 15-person team—the majority of whom are based in the United Kingdom—directly into Rivian’s autonomy and artificial intelligence division, which is led by James Philbin. According to Philbin, who spoke in an exclusive interview, the acquisition also provides Rivian with a strategic opportunity to expand its growing AI hub located in London.
Third Dimension was established in 2024 by CEO Tolga Kart and Piotr Sokółski. Prior to the acquisition, the startup successfully raised $7 million in seed funding from prominent venture capital firms, including Abstract Ventures, MVP, Soma Capital, and Solari Capital. Although the company’s underlying technology was originally engineered to accelerate and streamline video game development, the founders quickly recognized broader commercial applications for their software, identifying potential use cases in defense and autonomous vehicles.
Now, integrated within Rivian, the team will turn its full attention and engineering resources toward automated driving applications.
“The team is excited to be able to focus on one problem,” Tolga Kart shared during the interview. “I think as a small company, having to serve many, many different masters is very hard, and so we’re very excited to come work on a problem that has such an amount of scale, where our technology makes a true difference.”
Simulation has long been recognized as a critical tool for autonomous vehicle developers, who rely on virtual testing environments to train, test, and validate complex self-driving systems safely and efficiently. Historically, these automotive simulators have been built using traditional rendering engines or video game engines, frequently depending on synthetic data to populate virtual roads. While these virtual environments can closely mirror the real world visually, a persistent gap between the simulation and reality has remained a major hurdle for engineers, as Kart pointed out. The traditional workaround has typically involved feeding these simulators increasingly massive volumes of data in the hope of gradually narrowing that fidelity and quality gap.
Third Dimension’s proprietary technology utilizes artificial intelligence to accurately match the nuances of the physical world. When paired with Rivian’s vast repositories of real-world driving data, the resulting system has the potential to transform into an exceptionally valuable and precise simulation engine.
Rivian collects comprehensive driving data from tens of thousands of customer vehicles currently operating on public roads, including R1T pickup trucks, R1S SUVs, commercial delivery vans, and the newer R2 SUVs. This continuous data collection includes any vehicle whose owner has enabled external camera-sharing permissions. Rivian processes this information by aggregating and anonymizing the data before using it to build sophisticated training models for its driving software. According to Philbin, Rivian’s customer fleets upload more than 400,000 individual driving sequences every single day.
Integrating Third Dimension’s technology will allow Rivian to simulate complex, challenging, and unusual driving conditions effectively. By doing so, the engineering team can test software responses without needing to wait for rare real-world edge cases to happen naturally on public roadways, Philbin noted.