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Continuous driver identification and estimation of emotional state for semi-autonomous vehicles

  • Full or part time
  • Application Deadline
    Monday, February 10, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Face recognition can be used for robust driver recognition/verification. 3D face recognition is advantageous as it is not adversely affected by lighting conditions. 3D facial expressions can also be used to infer the emotional state of a driver. These tasks can be used simultaneously and continuously confirm the identity of a driver and to assess whether they are, for example, sufficiently angry or sleepy to adversely affect their driving.

This research also includes ethical problems such as the appropriate level of system transparency – should the system convey its estimate of emotional state back to the driver and, if it did, how would that alter driving behaviour? Should the system record the emotional state and who is responsible in the event of accident – the system for allowing the driver to drive whilst over-tired, or the driver? Does this system make the driver accountable for their emotional state, based on the automatic assessment using data from the 3D vision system?

For 3D face and expression recognition, photometric stereo offers a low cost and effective 3D image capture system. When coupled with the advanced recognition and expression classification algorithms to be developed by this research, continuous identification and emotional state monitoring can be performed. Identification can be used to ensure the driver is permitted to drive a particular vehicle. How the information on whether the driver is in an appropriate emotional state to drive and what actions a responsible, artificial intelligence-based autonomous system should take based on this information is an open question that this project will also explore.

This project is associated with the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its second cohort of at least 10 students to start in September 2020. Further details can be found at: http://www.bath.ac.uk/centres-for-doctoral-training/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree. A master’s level qualification would also be advantageous. Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Informal enquiries about the project should be directed to Dr Adrian Evans on email address .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP02&code2=0002

Start date: 28 September 2020.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum in 2019/20, increased annually in line with the GDP deflator) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

How good is research at University of Bath in Electrical and Electronic Engineering, Metallurgy and Materials?

FTE Category A staff submitted: 20.50

Research output data provided by the Research Excellence Framework (REF)

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