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Identifying and modelling car passengers’ profiles using advanced computational intelligence

Institute for Future Transport and Cities

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Dr R Iqbal No more applications being accepted Funded PhD Project (Students Worldwide)

About the Project

This PhD aims to utilize advanced machine learning algorithms and a multitude of inputs related to the passengers’ profile, to create a multimodal passenger profile detection system. This project leads to a novel system able to utilize in real-time combinations of input signals to intelligently identify and utilize the passenger’s profile in order to enhance the journey experience.

This research will explore the potential of utilizing in a synchronous and efficient manner video, audio, physiological signal information, and environmental inputs. Video input will be exploited with the help of computer vision methods responsible for capturing the passengers’ facial parameters and body stance. Audio signals (voice, yawning), physiological signals such as heart rate, and actions performed by the driver in the car will be also be inputs to the proposed classification system. The computational backbone of this project is the development and deployment of deep learning techniques. The potential of deep learning to handle big data cases resulted in creating a new generation of modelling and simulation applications. The classification results of the proposed system can be used to inform several services e.g. antitheft (detect a non-identified profile), changing the cabin configuration (temperature, music, luminosity) and others.

Coventry University
Coventry University has been voted ‘Modern University of the Year’ three times running by The Times/Sunday Times Good University Guide. Ranked in the UK’s top 15 (Guardian University Guide), we have a global reputation for high quality teaching and research with impact. Almost two-thirds (61%) of our research was judged ‘world leading’ or ‘internationally excellent’ in the Research Excellence Framework (REF) 2014. By joining the Institute for Future Transport and Cities, you will benefit from state-of-the-art facilities and partnerships with some of the biggest names in industry, including Jaguar Land Rover, GE Aviation, Cummins and Intel.

• Our research strategy is underpinned by a £250m investment in research and facilities
• Dedicated Doctoral College and Centre for Research Capability Development deliver high quality professional support for researchers, from PhD to Professor
• Free training: research career planning, managing your doctorate, research communication skills, research ethics, research impact, research integrity, research methods and research supervision
• Coventry is a member of the Doctoral Training Alliance (DTA), the largest multi-partner and only nationwide doctoral training initiative of its kind.

Candidate Specification
A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the Project element or equivalent with a minimum 60% overall module average.

In the event of a first degree classification of less than 2:1, a Masters Degree in a relevant subject area will be considered as an equivalent. The Masters must have been attained with overall marks at minimum merit level (60%). In addition, the dissertation or equivalent element in the Masters must also have been attained with a mark at minimum merit level (60%).


• the potential to engage in innovative research and to complete the PhD within a three-year period of study
• a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)

How to apply
Apply on line

Submitting full supporting documentation, and covering letter, plus an up-to 2000-word personal statement relating to the project

Start date: Jan 2021

Interview dates: Will be notified to short listed candidates

Duration of study: Full-Time – between three and three and a half years fixed term

Funding Notes

• PhD will be fully funded – 3.5 years (open to UK/EU and International candidates)
• Stipend - £15285 per annum to cover living / accommodation expenses.

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