Wayve, the UK startup and developer of Embodied AI technology for self-driving vehicles, has entered a strategic partnership with Uber on top of securing investment from the ride-hailing firm.

With the additional undisclosed funding and support from Uber, the UK startup intends to accelerate its work with original equipment manufacturers (OEMs) around the globe, enhancing consumer vehicles with advanced driver assistance and automated driving capabilities.

It’ll also work towards the development of globally scalable autonomous vehicles for future deployment on Uber.

The new partnership envisions future Wayve-powered self-driving vehicles being made available on Uber in multiple markets around the world, bringing the Embodied AI technology to Uber’s more than 150 million monthly global users.

“I’m excited to be teaming up with Uber, the largest mobility network in the world, to massively ramp up our AI’s fleet learning, ensuring our AV technology is safe and ready for global deployment across Uber’s network,” said Alex Kendall, co-founder and CEO of Wayve.

“Together, we’re excited to work with Automotive OEMs to bring autonomous driving technologies to consumers sooner.”

Dara Khosrowshahi, CEO of Uber, further added: “Uber and Wayve share a vision of reimagining mobility for the better.

“Wayve’s advanced Embodied AI approach holds a ton of promise as we work towards a world where modern vehicles are shared, electric and autonomous.

“We’re thrilled to bring Wayve on as a partner to work alongside automakers as we continue to build out Uber as the best network for self-driving vehicles.”

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The strategic partnership and investment announcement comes just months after Wayve raised $1.05 billion (£837m~) in its Series C funding round.

The significant funding round was led by Japanese conglomerate SoftBank Group, with contributions from new investor NVIDIA and existing backer Microsoft.

Founded in the UK in 2017, Wayve has been cited as a pioneer in Embodied AI for autonomous driving, being the first company to develop and trial an end-to-end (e2e) deep learning autonomous driving system on public roads.