"I’m here to help Five tackle the challenge of verifying autonomous vehicles. I have a clear aim: to help Five find ways of giving guarantees that Five’s cars are safe, so passengers can step inside the vehicle knowing it will do no harm to them or those around them."

"The general public will experience the car as a vehicle that performs journeys on our cities’ roads, getting people from A to B quickly and affordably. But, under the proverbial bonnet, there will be plenty more going on. These are not cars as we’ve known them. They’re autonomous in the sense that the car will be taking a number of decisions independently during the journey."

"With this in mind, we need to think about how we can give guarantees that the actions the vehicle will perform are safe with respect to its integrity and the environment. Let’s remember that we do not only have to consider the passengers but also the environment, which will be diverse and complex, encompassing other vehicles, pedestrians, cyclists, buildings, weather conditions, and more."

"To provide guarantees, we’ll of course perform extensive testing. This will give us some confidence about the way in which the vehicle will behave over a broad range of standard and corner cases. But testing is necessarily incomplete however extensive our testing, it can never be exhaustive. There are only a finite amount of situations one can test."

"A major challenge in deploying ML-based systems, such as ML-based computer vision, is the inherent difficulty in ensuring their performance in the operational design domain. The standard approach consists in extensively testing models against a wide collection of inputs. However, testing is inherently limited in coverage, and it is expensive in several domains."