PhD Studentship: Driver Behaviour Identification using In-car Sensors and Machine Learning

Application details
Reference number: WM022018
Start date of studentship: 1st April 2019
Closing date of advert: 1st January 2019
Interview date: TBC

Supervisors
Primary supervisor: Dr Will Midgley
Secondary supervisor: TBD

This project will take data from intelligent in-car sensors and use it to identify and characterise the driving behaviour of a set of drivers. The process will be refined to run in real-time, allowing the system to identify when drivers are behaving erratically and warn them about their driving. This project is a unique opportunity to work in collaboration with an innovative start-up based in Sydney, Australia and to have a real effect on helping make drivers safer and more efficient.

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.

Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/

Full Project Detail
The funder, GOFAR, is an innovative start-up based in Sydney, Australia. Their in-car dongle and “Ray” connect to most modern cars and allow users to track their fuel usage (among other things) and the Ray provides feedback on driver performance (red is bad, blue is good).

The aim of this project is to leverage this data to enable the app to make smart conclusions about the user’s driving. For example, it might suggest that a user change gear earlier to decrease fuel consumption, or it might notice that a driver is driving erratically and use the Ray to warn them to focus on the road.

This project will analyse a set of GOFAR data, and then aim to develop novel algorithms for understanding driver behaviour and presenting information to the driver, either through the Ray or the smartphone app.

Find out more: www.gofar.co.uk

Entry requirements
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Mechatronics Engineering, Computer Science, or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: MATLAB; Simulink; statistics and data analysis; machine learning; human factors training or experience.

Contact details
Name: Dr Will Midgley
Email address: [Email Address Removed]
Telephone number: 01509 227 040

How to apply
All applications should be made online at http://www.lboro.ac.uk/study/apply/research/. Under programme name, select Mechanical Engineering

Please quote reference number: WM022018