Autonomous vehicle startup Wayve has teamed with Microsoft to help support the development of AI-based models for autonomous vehicles (AVs) on a global scale. 

The tie-up will blend Wayve’s expertise using machine learning and vast quantities of data to train AI models with Microsoft’s engineering excellence in powering large-scale AI systems

The tie-up will bring together London-based Wayve’s expertise using machine learning and vast quantities of data to train AI models with Microsoft’s engineering excellence in powering large-scale AI systems. 

The announcement follows Microsoft’s participation in Wayve’s $200M Series B investment round earlier this year and also extends their existing collaboration, which has seen Wayve use Microsoft Azure since 2020 to help accelerate the development of its technology.  

Wayve said the partnership would bring self-driving technology to more places and customers sooner. Compared to traditional self-driving system that can take months and years to re-engineer for any new location, its data-driven ‘learned’ approach enables the design of AV systems that can generalise – or apply its driving intelligence – to new, previously unseen places. 

It’s already demonstrated this by testing its autonomous driving system across five new UK cities last year, showing it could perform the same driving skills it learned in London without any prior city-specific adaptations.  

The business has already partnered with Asda, Ocado Group and DPD to trial its AV technology and collect driving data from their operations in London.  

Commenting on the company’s latest tie-up, Alex Kendall, CEO of Wayve, said: “Joining forces with Microsoft to design the supercomputing infrastructure needed to accelerate deep learning for autonomous mobility is an opportunity that we are honoured to lead. Deep learning systems thrive on data, and we’ve put an immense amount of effort into understanding what it takes to get these systems on the road. We are excited by the opportunities that this collaboration will create as we push deep learning to new levels of scale.”