How do humans and machines safely share control of an autonomous car?

Ensuring and assuring the safety of shared control in autonomous driving is very challenging due to the uncertainties associated with measuring the level of situational awareness of safety drivers while not in control of the vehicle, and with the mapping of such measures to control hand-back times and likelihood of success.

This project will extend, adapt and integrate the team's recent research and the latest advances from human behaviour and cognitive modelling, verification of deep neural networks, and automated controller synthesis to tackle these challenges.

An advanced semi-autonomous driving simulator will be used to deliver methods for ensuring and assuring the safety of shared control in autonomous driving. The project will make significant and generalisable contributions in the areas of:

  • shared autonomy
  • training and verification of machine learning
  • monitoring of autonomous systems by human operators

Project progress

Four new PhD students have been recruited: one at the University of York, one at Carnegie Mellon University, and two at the University of Virginia (UVA). The team has designed and conducted a human subject study on the driving simulator located at UVA involving 18 participants. This collected data about a human’s response time and physiological signals in different driving scenarios. 

Using the preliminary data from this study, the team trained a neural network model that achieves 90% accuracy of driver takeover time prediction (with the takeover time organised into several categories, e.g. fast, medium and slow). Work has also started to explore the verification of such a neural network and to synthesise a safety controller that will exploit the predictions provided by the neural network model.

Project team

  • University of Virginia
  • Carnegie Mellon University
  • University of York