Enhancing Driver Performance and Safety, using Simulated Vehicle Data Analytics

Summary

This research aims to enhance driver performance and safety by utilising vehicle and user telemetry data from simulated environments. By analysing key inputs such as steering, braking, and throttle, the project seeks to improve consistency and driving skills for both road and track scenarios, combined with data acquired from heterogeneous sources, such as eye tracking, GSR, and heart-rate. This simulation-based approach will also contribute to environmental sustainability by helping drivers optimise their driving with regard to braking, cornering, gear utilisation and thereby impacting fuel consumption and brake and tyre wear, without the need for real cars and tracks to be used.

The study will leverage advanced technologies, including motion sensors, eye tracking, and driver focus monitoring, to capture driving behaviours in a realistic but risk-free environment. These data streams will be processed using cutting-edge techniques such as machine learning (ML), large language models (LLM), and process mining. The aim is to develop a digital twin framework that facilitates the collection, processing, and analysis of anonymised driver data to identify best practices and optimize driving.

By modelling the actions of novice to expert drivers across various terrains and conditions, including weather simulations, this project will help classify and recommend desirable driving behaviours. The findings will be particularly useful for examiners and instructors in evaluating driver capabilities, ensuring that safety and performance are optimized for both fossil-fuelled, hybrid, electric and hydrogen vehicles.

The project aligns with the University’s strategic focus on sustainability, digital transformation, and human-computer interaction, supported by state-of-the-art simulation equipment available within the School of Computing. The outcomes will provide valuable insights into the future of driver training, autonomous vehicle safety, and transportation technology.

Research Area:
Investigation of machine learning (ML), large language models (LLM), and process mining to aid interpretation of telemetry data.

https://youtu.be/Dzasl3mBiII?si=JgJIfK9S4Df4HPtU (1min video)

The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. We particularly welcome female applicants, as they are under-represented within the School.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.