https://wayve.ai/blog/unlocking-markets ... eneralise/
In this post, we discuss the results of a recent multi-city generalisation test, conducted to explore how we are building the most scalable approach to autonomous driving.
To build autonomous driving technology that can easily scale to new markets, we are pioneering a data-driven approach to self-driving. At the heart of our AV2.0 platform is a fully learned end-to-end motion planner that can quickly and safely adapt to complex driving environments, anywhere in the world. Our full AV2.0 platform consists of a camera-led sensor suite, an end-to-end neural motion planner and an autonomous driving system designed with safety and redundancy in mind.
Limits of the traditional approach
It is interesting to contrast AV2.0 with what is being widely used in the AV industry today, what we call AV1.0. The traditional approach is a modular perception-prediction-planner stack derived from classical robotics principles. AV1.0 is, broadly speaking, motivated by the general principle that if perception is solved then motion planning is easy. This unfortunately is yet to be proven despite years of engineering efforts and billions in investment by numerous companies.
Well engineered AV1.0 stacks that follow this modular design principle have the benefits of being able to engineer in parallel each of the modules of perception, planning, and control independently. However, these stacks are very expensive to design, adapt and maintain, and are reliant on expensive hardware, HD mapping, and localization systems. These stacks are also brittle as they place extremely high demands on the sensing and perception modules. Furthermore, interfaces between the fundamental modules need constant adaptation, and errors propagate throughout the stack. These planners, although evolving from classical algorithms to more data-driven ones, still suffer from perception and localization errors.
Our alternative vision
We reframe the driving problem as one that can be solved fully using machine learning, i.e., jointly learning to represent any driving scene and motion plan using a deep neural network trained on large quantities of human driving demonstrations. This approach enables us to build an autonomous mobility platform that can quickly and safely adapt to new cities, use-cases, and vehicle types, which is a core promise of AV2.0. Achieving this is game-changing for scaling autonomy and it means that we can deploy in new markets faster with substantially lower cost; moving us closer towards our goal of being the first to bring AVs to 100 cities.
Developing AVs that can easily drive in new places is significantly different from concurrent AV industry methods which require time-consuming and expensive city-specific adaptations such as building and maintaining highly-detailed and customized HD maps for every road driven.
To deploy in a new location, an AV1.0 team starts by manually driving sensor-equipped vehicles down every street so they can paint a 3D picture of the environment, down to the centimeter. They process this data into a detailed map with additional context such as speed limits, lanes, and traffic light locations. The maps are then tested and verified before being deployed, as well as constantly updated as city streets change. With the environment mapped, now comes the challenge of adapting the behavior planning for the new driving culture and environment. This is notably difficult, even in moving between two seemingly similar environments. The solution to this looks like an engineering team redesigning components of a large, complex planner. This process takes months.
In contrast, at Wayve, we are building AVs that generalise and are intelligent enough to not need these cumbersome HD maps. The world is constantly changing, so we need to be able to adapt to drive anywhere. What we mean by this is we can train our AV2.0 system to learn to drive autonomously on, say, London roads and then it can apply this acquired driving skill to new, unseen places and cities without any place or city-specific adaptation.
How did we test this?
To demonstrate this capability, we recently conducted a multi-city generalisation test where we took our best performing AV2.0 model to 5 different cities across the UK that we have never previously been to. The goal was to see if our AV2.0 model that was trained in London could generalise its driving intelligence to new cities, with no prior data collection to influence model performance in the new cities.
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