This is an interesting and novel approach to self driving cars that is very different from anything else that is being used. Wayve is “simply” allowing onboard safety drivers to teach the cars how to driving using onboard AI, instead of using a series of maps, exotic sensors, and 10s of millions of parameters coded. Thanks @cageymaru.



Our network architecture was a deep network with 4 convolutional layers and 3 fully connected layers with a total of just under 10k parameters. For comparison, state of the art image classification architectures have 10s of millions of parameters.

Wayve has a philosophy that to build robotic intelligence we do not need massive models, fancy sensors and endless data. What we need is a clever training process that learns rapidly and efficiently, like in our video above. Hand-engineered approaches to the self-driving problem have reached an unsatisfactory glass ceiling in performance. Wayve is attempting to unlock autonomous driving capabilities with smarter machine learning.

Imagine deploying a fleet of autonomous cars, with a driving algorithm which initially is 95% the quality of a human driver. Such a system would not be wobbly like the randomly initialised model in our demonstration video, but rather would be almost capable of dealing with traffic lights, roundabouts, intersections, etc. After a full day of driving and on-line improvement from human-safety driver take over, perhaps the system would improve to 96%. After a week, 98%. After a month, 99%. After a few months, the system may be super-human, having benefited from the feedback of many different safety drivers.

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Source: [H]ardOCP – Wavye Teaching a Car to Drive