Wayve’s technology relies on machine learning instead of the conventional method of relying on detailed digital maps and coding

British startup Wayve said on Wednesday it will use supercomputer infrastructure designed for the firm by its investor Microsoft to process vast amounts of data as it develops machine learning-based models for self-driving cars.

Wayve’s technology relies on machine learning using camera sensors fitted on the outside of the vehicle, where the system learns from traffic patterns and the behaviour of other drivers, instead of the conventional method of relying on detailed digital maps and coding to tell vehicles how to operate.

Mark Russinovich, CTO, Azure at Microsoft said: Supercomputing capabilities are key to processing the immense amount of data required for the simulation, validation, and training of AI models that enable safe and secure autonomous driving.

Wayve is combining its expertise in deep learning-based autonomous vehicle systems with Microsoft Azure computing power to bring self-driving transportation experiences to more people and organisations faster, he said.

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.

That will help the startup as it scales up its self-driving technology for trials on last-mile delivery vehicles with UK online grocery technology company Ocado and supermarket chain Asda.

Those grocery delivery trials will start this year with a human safety operator on board.

Earlier this year, Microsoft participated in the London-based startup’s $200 million Series B funding round.