Fleet Learning Technology

As new Wayve vehicles take to the road in new cities, our Fleet Learning Technology continually improves the AI software that drives them.

Fleet Learning Technology

What makes AV2.0 unique is that our intelligent autonomy software constantly improves from every mile driven across all fleet deployments. Our Fleet Learning Technology enables this network effect.

This platform brings together all of the capabilities needed to train, evaluate, and deploy machine learning systems that can operate safely at scale in the real world. Today, we’re building technology to collect data from fleets of vehicles, train models on vast amounts of driving data, create simulated worlds where it can learn how to drive in different environments and situations, and ensure that these models are safe before they’re deployed on real roads.

Fleet Learning Technology

Large scale driving data

Data is a key ingredient for our autonomous driving software to learn how to drive safely and reliably in different environments. It is also crucial in evaluating the performance of our AI Driver.

We’ve built a robust system for collecting and analysing data from all on-road experiences and simulated environments to enable continuous learning. We continually expand our driving datasets to cover various environments, such as urban and suburban roads, during the day and night and in many weather conditions.

Image of the driving data that Wayve processes

Groundbreaking MLOps

To ensure our models are developed responsibly, we’re developing groundbreaking MLops workflows—creating new tools, processes and pipelines that allow us to build, train and deploy billion-parameter AI models on vehicles more quickly and confidently.

We’re building new methods to manage our foundation models faster and more confidently across the entire machine learning lifecycle so that we can demonstrate that our AI Driver is safe and trustworthy.

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