Wayve

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London-based Wayve pioneered an autonomous driving system based on “end-to-end deep learning,” which feeds data from a vehicle’s sensors into a neural network that oversees the vehicle’s navigation, rather than relying on a preprogrammed set of rules.
Wayve’s approach has become more common among self-driving competitors as AI capabilities have improved. The startup has raised more than $1&nbsp;billion from investors including Nvidia and Microsoft. Next year it will launch self-driving taxi trials in London in partnership with Uber, and Nissan plans to incorporate Wayve’s software into its cars beginning in&nbsp;2027.

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London, a city of narrow, winding streets and omnipresent cyclists, is a famously difficult place to drive. That helps explain why Bill Gates went viral in 2023 when he posted a video of himself touring the city in a self-driving car, powered by Wayve. The autonomous-driving field is crowded, but Wayve’s platform stands out in two notable ways: It can run on just one GPU chip, and it uses an LLM to communicate with drivers about why it’s doing what it’s doing at any given moment. Microsoft and Uber are backers, and SoftBank and Nvidia took part in a $1.1&nbsp;billion funding round in May—the largest to date by a Europe-based AI company. (Wayve has not publicly disclosed its valuation.)

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Wayve Company Profile, Stock Price, News, Rankings

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Wayve | Fortune

Wayve Company Profile, Stock Price, News, Rankings | Fortune

Wayve CEO Alex Kendall takes Fortune’s Jeremy Kahn for a ride in London, showcasing the mechanics of the company’s AI-powered vehicle running on a single GPU

Microsoft, NVIDIA, and SoftBank have bet over $1 billion on this self-driving startup—here’s what it’s like to take a ride

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Microsoft, NVIDIA, and SoftBank have bet over $1 billion on this self-driving startup

Just engaged autonomy and look at that. The A I is now controlling the speed, the steering, everything about the vehicle and we're straight into traffic. Let's watch how we safely navigate around that cyclist. You can see we're slowing down, giving us some distance. So we're going to make a left turn, which would be interesting to see. So, yeah, we should slow down and see how we let the cyclist go through and then make the left turn when we moved over to make a left turn and we had to pick which lane we were going to, was it wanting to that? It knows from mapping that, that it has to be the left lane or did it actually read the indications on the roadway? So it gets to simply navigation from like drive straight or take a lift here or things like this a bit like what you might get if you use a set net system, some of the way the car is control the speed it chooses or the line it takes. None of this is hand programmed, not following a map, but it's making all of these decisions based on what it sees and the whole driving models on a single GP U in the car. Yeah. Yeah, there's nothing on the cloud. It's all on board the vehicle on a single GP U. A similar amount of computes to what we're seeing in production vehicles today. How did you gather the training data for the system? We use a wide variety of sources of training data to make sure that we learn the most safe and performance system. So of course, we have our own fleet that we test throughout the day and night, getting experience in London and we do some trips to other cities as well. From time to time, we partnered with ASDA and 02 of the largest grocery fleets here. Together we collect data from their grocery fleet and we have some other initiatives like that, that we, we're sort of taking off around the world. You mentioned, it's sort of like a large language model takes in all of that data, including things that you wouldn't necessarily want the model models behavior on. But having that amount of data, it sort of makes the system more robust in the end. But then of course, with large language models that you do have that sort of reinforcement learning through human feedback process. Is there some similar equivalent thing that you do with the data? Yeah, it is. And the fact that the way that we test and we give the feedback or in simulation when we resim and look to understand how the system performs. It's, it's a, it's very similar form of feedback in general. The way that, you know, I've been, I've been working on these A I models for over a decade now and I've seen breakthroughs in computer vision or robotics or natural language. They all tend to converge we're seeing now the same techniques apply in different fields. So the rise of synthetic data, for example, in large language models, we see the same thing with generative A I and what we can do in simulation for driving multi. Now we have vision uh language models, you know, say with the LLN similarly, we have lingo which gives us language capabilities in in this car. So all of these kind of trends are playing out in a very similar way. Of course, the interesting thing in this space is that we have the integration problem of making it run real time in a car and the safety challenges of running in the open world where we can't control what might happen. Ultimately, I think what we're learning is that A I is breaking through a lot of the glass ceiling that we've seen in the industry so far, removing the need for maps and being able to leave the geo fence, enabling the use of cost-effective hardware and sensors. Uh But more importantly, solving the long tail of cases and producing behavior that's intelligent enough to drive in complex environments like London

Wayve

Wayve in Fortune Rankings

Wayve in Fortune Rankings

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