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Control Transitions Between Human Driver and Artificial Intelligence

Department of Aeronautical and Automotive Engineering

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Dr M Martínez , Dr M Best , Dr Eve Zhang No more applications being accepted Funded PhD Project (European/UK Students Only)
Loughborough United Kingdom Aerospace Engineering Applied Mathematics Data Analysis Computer Science Software Engineering Statistics

About the Project

Reference number: AAE-MM-2013

Start date of studentship: 1 July 2021

Closing date of advert: 16 April 2021

Interview date: April/May 2021

Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career.

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Project introduction:

While full automation of road vehicles remains a very challenging goal, shared-control and semiautonomous driving are more feasible objectives in the near future. These alternative driving modes will benefit from new research towards how the control of the vehicle can be transitioned from the AI to the human driver and vice-versa in a seamless manner. This project consists of studying the use of machine learning and artificial intelligence to handle these control transitions.


This project will involve doing research in a vibrant research environment in the Department of Aeronautical and Automotive Engineering at Loughborough University. Upon successful completion, the PhD researcher will attain expert level within the topics of intelligent transportation, machine learning and human-machine interaction, with ample career openings both in the automotive industry and academia.


Full Project Details:


Full automation of road vehicles has received widespread media coverage, but it remains a very difficult objective. However, various shared-control modalities, in which the control of vehicle (or machine) is shared between the human and the newly incorporated vehicle intelligence, offer more realistic and viable alternatives.


The Department for Transport has issued a consultation plan to incorporate and legislate autonomous and semi-autonomous systems in the UK roads. Effective protocols and methodologies that can handle control transitions between the human and the machine in a safe and smooth manner are yet to be developed.


This research project can benefit from new data science trends, such as deep learning, statistical learning and machine learning in general. The aim is to study systems that are efficient in transferring the control between different agents in different driving conditions. This research is also applicable to the control of other types of vehicles and machines beyond road vehicles.


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Entry requirements:

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent). A relevant Master’s degree and/or experience will be an advantage. This project is interdisciplinary and candidates from different backgrounds (engineering/mathematics/computer science) are welcome. The candidates should be knowledgeable in at least one of the following: artificial intelligence (machine learning), vehicle control or computer science. A minimum level of programming experience is required.

Contact details:

Name: Dr Miguel Martínez

Email address: [Email Address Removed]

Telephone number: +44 (0)1509 227331


How to apply:


All applications should be made online at Under programme name please select: ‘Automotive Engineering’


Please quote reference number: AAE-MM-2013

Funding Notes

The studentship provides a tax-free stipend of £15,285 per annum for 3 years plus tuition fees at the UK/EU rate. International (non-EU) students may apply, however the total value of the studentship will be used towards the cost of the international tuition fee.

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