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Computer Science: KESS II Funded MSc by Research Studentship at Swansea University: Visualising Driver Behaviour using Sensor Data and Diagnostic Trouble Codes

Swansea University is a UK top 30 institution for research excellence (Research Excellence Framework 2014), and has been named Welsh University of the Year 2017 by The Times and Sunday Times Good University Guide.

*This scholarships is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for West Wales and the Valleys.*

In recent years, there has been an increase in the number of automotive manufacturers and insurance companies that are collecting vehicle telematics data. This generally involves the installation of technology on the vehicle to collect and broadcast specific vehicle state data in real time. The data usually comes in the form of sensor readings from various major components across the vehicle. Typical sensor data fields include:

Temperatures (engine coolant, transmission fluid, internal, etc.),Fuel levelsSpeedEngine on/offPressures (engine oil, air filter, etc.)Position (coordinates)

Additionally, more refined information can be ascertained from event-driven Diagnostic Trouble Codes (DTCs). A DTC event arises when sensor data on the vehicle meet/exceed pre-defined engineering thresholds, e.g. 'engine temperature too high'. DTCs adhere to standards published by the Society of Automotive Engineers (SAE).

We Predict wants to capitalise on this market opportunity to provide predictive analytics based on real time Telematics and requires visualisation expertise input to develop a market ready solution. The purpose of this project is to represent the complexity of this inter-related sensor data alongside other associated data and resultant failures in a way that is easy to interpret and elucidates relationships otherwise inscrutable. Specifically, the work to be carried out by the student in this project will be to explore, experiment and test visualisation formats and techniques to arrive at this optimum presentation working with appropriate machine learning approaches for data summarisation.

Scholarships are collaborative awards with external partners including SME’s and micro companies, as well as public and third sector organisations. The scholarship provides 1 year funding with a 3 month period to complete the thesis. The achievement of a postgraduate skills development award, PSDA, is compulsory for each KESS II scholar and is based on a 30 credit award.

Eligibility

Candidates should have a 2.1 or above in their undergraduate degree in computer science or a related subject. They should also be eligible for UK/EU Fees.

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Benefits

The studentship covers the full cost of UK/EU tuition fees, plus a stipend.  The bursary will be limited to a maximum of £11,472 p.a. dependent upon the applicant’s financial circumstances.

There will also be additional funds available for research expenses.

Application

Please visit our website for more information.

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