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Wayve is Using Deep Learning to Drive Like a Human

This is a must read. Wayve postures that humans have a remarkable ability to learn to drive quickly and can obtain a licence to drive across a whole country after tens of hours of practice.

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Wayve Uses Deep Learning

From a press release by Wayve.

But after 10 years of commercial self-driving car development, over 10 million autonomous miles and $5B per year spent, we still do not have commercial self-driving vehicles on our roads. To turn what is currently a fantasy into a reality, we need to take a different approach.

The solution is machine learning, which is surpassing hand-engineered systems everywhere. Intelligent behaviour cannot be hand-coded, but can be learned through experience. We’ve built a system which can drive like a human, using only cameras and a sat-nav. This is only possible with end-to-end machine learning.

No HD-Maps,no expensive sensor/compute suite,no hand-coded rules and no driving on roads never-seen during training.

This scales self-driving technology like never before: for everyone, everywhere.

In this video you can see our system driving on public roads in Cambridge, UK. It’s driving on roads it has never been on before using just a simple sat-nav route map and basic cameras. We don’t tell the car how to drive with hand coded rules: everything is learned from data. This allows us to navigate complex, narrow urban European streets for the first time.

Why is our technology different? It learns end-to-end with imitation learning and reinforcement learning to drive like a human, using computer vision to follow a route. Imitation learning allows us to copy behaviours of expert human drivers. Reinforcement learning lets us learn from each safety driver intervention to improve our driving policy.

Our model learns both lateral and longitudinal control (steering and acceleration) of the vehicle with end-to-end deep learning. We propagate uncertainty throughout the model. This allows us to learn features from the input data which are most relevant for control, making computation very efficient. In fact, everything operates on the equivalent of a modern laptop computer. This massively reduces our sensor & compute cost (and power requirements) to less than 10% of traditional approaches.

This could change everything.

For the complete press release click here.

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