Several notable angel investors also participated in the round, including Uber’s chief scientist Zoubin Ghahramani and Pieter Abbeel, a UC Berkeley robotics professor and pioneer of deep reinforcement learning.
Founded out of Cambridge, U.K., in 2017, Wayve’s core premise is that the big breakthrough in self-driving cars will come from better AI brains rather than more sensors or “hand-coded” rules. The company said that it trains its autonomous driving system using simulated environments and then transfers that knowledge into the real world, where it emulates how humans adapt to conditions in real time. Wayve’s systems learn from each safety driver intervention to understand why the driver had to intervene, bypassing HD maps, lidar, and other sensors that have become synonymous with the burgeoning autonomous vehicle movement.
It is worth noting here that Wayve’s machine learning algorithms can work in tandem with any hardware or sensors if that is how an automaker wants to use them, but Wayve’s central pitch is that autonomous cars should be able to learn new environments just like humans do.
“Our algorithms are learning to become super-human drivers,” Wayve Co-Founder and CTO Alex Kendall told VentureBeat. “We learn from attentive human driving, which already eliminates the 98.3% of human road errors due to inattention / ineffective driving. We then further improve beyond what humans are capable of with reinforcement learning, by providing feedback to our system.”
Wayve insists it can build a safe and effective self-driving vehicle system using end-to-end machine learning, basic cameras, and GPS navigation.
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