Future

Bridging virtual and real-life racing to train AI racing cars

by Samarth Kanal

5min read

A2RL virtual cars

Artificial Intelligence could have a host of applications in motorsport, and one of those is to further self-driving technology by using the track as a testbed. A2RL is doing just that - but taking its efforts to the virtual world to bolster its real-world findings.

Aston Martin F1 car exiting garage

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Such an approach draws parallels to the development loop in high-level motorsport series, where tools such as simulators and computational fluid dynamics are used in parallel with track testing. 


In A2RL - the Abu Dhabi Autonomous Racing League - this ‘loop’ includes a virtual rendition of the track to further teams’ software development.

Raceteq has covered the mission of A2RL in the past, detailing its ambition to hone autonomous driving technology by pitting teams of engineers against each other as they programme cars to race each other on track.
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Past events at Abu Dhabi’s Yas Marina Circuit and Japan’s Suzuka have demonstrated the potential of A2RL’s self-driven cars that use AI stacks - combinations of sensors including Lidar and cameras plus machine learning software - to navigate the track.


The series has even hosted ‘man versus machine’ spectacles involving ex-Formula 1 racer Daniil Kvyat, who has bested the AI cars with ease.

But the A2RL teams, consisting of engineers from major universities around the world, and their respective machines, based on Japanese Super Formula cars, are improving. As with any motorsport series, track testing is at a premium - so A2RL has introduced the ‘SIM-Sprint’ challenge.


This is a cloud-based simulation environment supplied by a US-based company that recreates A2RL’s single-seater racing cars, tracks and various conditions to help the teams strengthen their machines and their learning to handle real-life racing.

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Ex-F1 driver Daniil Kvyat racing an AI-driven car in 2024 at Yas Marina Circuit

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A2RL’s teams therefore compete in four virtual races before a real-life finale at Yas Marina Circuit in November 2025, with prize money on the table. They are judged on their cars’ perception, reaction speed, and consistency. 


A2RL’s head of sporting management explains to Raceteq that the virtual platform can be run locally by teams and they have free rein to practice on fictional circuits or Yas Marina Circuit in their own time - even hosting their own races against other teams as one would on a racing game or simulator.

The reason, he says, is that FIA-grade circuits “aren’t cheap to rent - plus the teams need to fly down here, there's hotel costs, there's food and beverage, et cetera, associated with it".


And that’s not including the fuel, tyres, and mechanics that A2RL supplies.

Winkler adds: “So we need to see how we can leverage the biggest benefits out of the simulator and apply those into real competition”.


A2RL showed its autonomous machines to the world in 2024 as its Yas Marina season finale was broadcast live around the globe. The cars struggled to complete a lap of the circuit, sometimes stopping in their tracks when another rival was in the vicinity. 

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A virtual rendering of the A2RL AI cars at Yas Marina Circuit

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Winkler says the issues were “mainly due to the fact that the teams didn’t have… any training with multiple vehicles besides just one other car being on track".


He explains: “So the sim is meant to shorten that learning curve… it's helping the team spend more time to go slowly and understand how other vehicles behave on track because every algorithm is different. It's like every driver is different - they have different braking styles, they have different levels of risk taking, some are more aggressive, some are more passive.”

“There is another benefit of introducing A2RL SIM-Sprint,” says Winkler. 


“It provides a low-cost and accessible route into the sport - similar to how karting feeds into Formula 1. Eventually, we would like to open the competition to a wider community of participants, including fans, independent developers, and aspiring teams worldwide. It would provide a structured pathway for talent to enter the league and contribute to the future of high-performance autonomy.”

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Fireworks light up the sky at the inaugural A2RL season finale in 2024

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The A2RL car is a 2023 Super Formula SF23 car, capable of hitting 200 miles per hour, but it didn’t hit those speeds in 2024. Another series, the Indy Autonomous Challenge, holds the speed record (as of August 2025) for an autonomous vehicle: 197.7mph, set by a Maserati MC20 at Florida’s Cape Canaveral in March 2025.

The 2025 A2RL car includes what operations lead Joshua Roles says is a “completely overhauled” autonomous system with new Lidar technology, new radars, multiple camera arrays and navigation sensors that allow for “more accurate positioning, obstacle recognition, and situational awareness”. 


That includes new bespoke technology that can better handle the G-forces, heat, and vibrations on track.

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The Indy Autonomous Challenge’s record-breaking AI-driven Maserati MC20 at Cape Canaveral

“One of the biggest challenges for us at A2RL has been reducing system latency,” adds Roles. “To outperform a human driver, we don’t just need a faster platform; we need one that can make multiple decisions simultaneously and faster than a human ever could.”


Right now, every team has the same hardware, but that could change. 

“In the future, we expect motorsport engineers and professionals to be fully integrated into the coding teams, enabling a more collaborative approach where the car is no longer a locked system. 


“Teams will be able to make set-up adjustments - such as aerodynamic balance, tyre pressures, and other race-critical parameters - just as they would in traditional motorsport,” says Roles.

The long-term strategy of A2RL remains the same: it works to further autonomous driving technology through motorsport. 


With one lap of Yas Marina Circuit generating 200 gigabytes of sensor and telemetry data - and a single full-day test capturing up to 24 terabytes of data - there’s lots to be learned from running an AI machine on track. 

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