Close-up of driver's hands behind the steering wheel of a car
Photo: Chalmers

New collaborative project aims to solve safety issues with autonomous vehicles


AI and machine learning have accelerated the development of autonomous vehicles. By training the car's systems on a vast amount of data, the car can now recognize a parking space, a cyclist on the road, or a small child on the sidewalk. But how much data is enough to ensure that the vehicle does not make mistakes? How accurate does the data and its labels (parking, cyclist, child) have to be? These, and other questions, are what the FAMER project is set to answer.

"We aim to develop tools which will help all stakeholders in the manufacturing process to collaborate and reach a point where everyone feels confident that both the quantity and quality of the data are sufficient to ensure the system’s safety," says Eric Knauss, professor at the Department of Computer Science and Engineering. 

In September he launched the FAMER project (Facilitating Multi-Party Engineering of Requirements), which is set to span three years and is funded by Vinnova. The project is coordinated by the University of Gothenburg and partners include Kognic, RISE, Volvo Cars and Zenseact.

Read the full article on the department's main webpage