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Uber invests in UK startup Wayve to speed up self-driving projects

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FILE PHOTO: A sign marks the offices of Uber in Saugus, Massachusetts, U.S., May 8, 2019.   REUTERS/Brian Snyder/File Photo
A sign marks the offices of Uber in Saugus, Massachusetts, U.S., May 8, 2019. The ride-hailing giant has invested an undisclosed sum in the British startup Wayve to help develop autonomous driving software. REUTERS/Brian Snyder/File Photo

LONDON – Ride-hailing platform Uber will invest in Wayve to help the British self-driving technology startup work with major automakers to speed up the development of its systems, the two companies said on Thursday.

The companies did not disclose the size of the investment. But they said it would be added to the $1.05 billion in Series C funding Wayve announced in May, which was led by SoftBank Group.

Wayve has developed “Embodied AI” technology that it says can learn from and adapt to human behaviour.

The company said it would use the funding and support to accelerate the development of Level 2 and Level 3 driver assistance and automated driving functions with automakers, “while also working towards the development of globally scalable Level 4 autonomous vehicles for future deployment on Uber.”

Advanced functions

Level 2 systems are designed to perform basic tasks autonomously, such as staying in lane or maintaining a set distance from other cars, while Level 3 cars are meant to drive themselves, but require the driver to pay attention.

Level 4 autonomous vehicles are designed to drive themselves under most circumstances and take action on their own in dangerous situations.

While automakers and autonomous vehicle software companies have had more success with basic driver assistance functions such as lane-keeping systems, developing vehicles that can truly drive themselves has proven more difficult and much more expensive than many had expected.

Among the main challenges is that self-driving software systems have lacked humans’ ability to predict and assess risk quickly, especially when encountering unexpected incidents or “edge cases” where autonomous vehicles get stuck because they cannot work out what to do.

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