AI in motion

While there has been fast and rapid progress in terms of the development of self driving vehicles, these new technologies have also been controversially involved in a number of fatal accidents. This project studies the “social order” of traffic. What happens when artificial and human intelligence meet on the road?

As autonomous and semi-autonomous vehicles have grown in sophistication and ability, they have been deployed on the road in increasing numbers. While these technologies have driven millions of miles, they have also arrived in a partially broken form – and been involved in a number of fatal accidents.

Within sociology, research has documented the “sociality of traffic” – the taken for granted unseen order that makes public space effective and safe; yet the current generation of autonomous systems has little understanding of this.

If our public spaces are increasingly becoming a mix of “AI” and “human” vehicles, what will happen to this social order of traffic? Will the burden of making autonomous vehicles work fall onto those who do not have access to these vehicles – pedestrians, cyclists and other road users?

The goal of this project is to understand the new interactional public space of human and AI drivers. We see the emerging problems here as “relentlessly interactional” – in the moment by moment co-ordination of road users reflexively moving together. The project will document how humans change their expectations and behaviours when meeting AI-controlled vehicles, but also how those ongoing interactions in turn affect other road users.

To look at this we will collect video recordings in a range of different contexts – from existing semi-autonomous systems on the road, experimental test vehicles (at Nissan’s research laboratory in the USA) and current deployments on Swedish roads (with autonomous buses).

This data will let us conceptualise the role of interactive AI-systems, systems in continual interaction with humans, with real-time adjustments on both sides. The outputs from this project will support a better understanding of the social consequences of “mobile AI” in terms of driver training, policy making and regulation. In turn, this will also inform the building of better autonomous vehicles.