EngD Project: Sustainable luxury
This project aims to identify the requirements needed for luxury brands to achieve long-term profitable growth in a sustainable future. Sustainability and luxury can seem paradoxical at first glance, but a deeper understanding of the two uncovers similarities fundamental to both such as timelessness, durability, innovation and a respect for materials. The project scope will initially include identifying consumer perceptions in order to understand implications for design, communications and manufacture.
Supervisors: Dr Rebecca Cain, Dr Kerry Kirwan and Dr Steve Maggs
PhD Project: Online Patient Feedback in the NHS
Patients are embracing the opportunity to review their experience of receiving healthcare online. This research uses a mixed methods approach to explore GPs attitudes to online patient feedback, in particular the value that GPs attach to online patient feedback; the usefulness of the feedback for quality improvement; how the transparent ‘TripAdvisor’ style of the feedback makes GPs feel and GPs general perceptions of the use of social media as a tool to gain patient feedback on their services. The findings will be of use to the creators of online feedback services for healthcare.
Supervisors: Dr Rebecca Cain, and Prof Lucy Hooberman / Dr Kevin Neailey
EngD project: To validate WMG’s 3xD vehicle simulator and assess the Human factors concerns within self-learning and autonomous vehicles
Automation in road vehicles is becoming ever more common, the automotive industry is rapidly heading towards more autonomous systems, with the eventual goal of fully ‘Self-driving’ cars. WMG’s ‘3xD Simulator for Intelligent Vehicles’ is a key tool for the development of future vehicles and it is where this doctoral research is focused. Through validating the 3xD simulator as a research tool and ensuring transferability of simulated to real-world experimentation data we can gain a deep understanding of the impact of future technologies for road users. With this comprehensive understanding of the validity and transferability of vehicle simulation, a new era of rapid-prototype automotive research can be realised. This project will be working closely with JLR’s Self Learning Car team and it is concerned with the Human Factors aspect of self-learning features and increased automation.
Supervisors: Prof. Paul Jennings and Dr. Stuart Birrell
EngD project: Exploring the user experience of autonomous vehicles
The focus of this EngD is to explore how user experiences will evolve as autonomous vehicles become more pertinent in society. Current solutions to the intelligent vehicles paradigm suggest the ideal interface is a touchscreen in the dashboard, but natural user interfaces like voice and gestures have a huge, but as yet untested potential. More specifically, the EngD aims to answer questions on the use of sound, light and new digital interfaces in the car. Moreover, the WMG 3xD Simulator for Intelligent Vehicles gives the research a robust basis on which these questions can be explored.
Supervisors: Dr Rebecca Cain and Dr Stewart Birrell
Joint projects with Energy and Electrical Systems team:
Claudia Geitner - EngD Project: The Disctration Free Car
This project aims to understand and minimise driving distraction and optimal level of workload as a result of using in car interfaces. The problem can be tackled in at least three ways. One option is to provide a monitoring system in the car reminding the driver when they spend insufficient attention on the driving task. Another option is to offer additional tasks to the driver in times of reduced workload. Non-driving related tasks could be presented in a form that does not require too much attention from the driver enabling them to respond adequately to changing road situations, e.g. using alternative sensory channels. Alternative interfaces could offer auditory, gesture or tactile interaction. The project contributes in different ways to reduce distraction and workload in cars. Involving a rethink of the in-vehicle interfaces to make them less distracting and actually contribute to safe driving, additionally by organising the driver distraction measures and workload which can then be used for the evaluation of the newly developed interfaces.
Supervisors: Dr Stewart Birrell and Professor Paul Jennings
Vadim Melnicuk - EngD Project: Integration of consumer electronic devices into premium vehicles for human interaction
The focus of this EngD is how best to integrate CE devices into the premium vehicle, not from a technological point of view, but a Human Factors perspective. Furthermore, it investigates the use of existing Consumer Electronic features, such as face and voice recognition for driver identification or personalisation, or cameras and face detection for safety related feedback. Consumer electronic (CE) devices, e.g. Smartphones, tablets and wearable technology, is a high value growth area both in terms of technological development and market penetration. With this technology ubiquitous in our everyday lives it is not surprising that a recent study showed that 92% of drivers admitted to using their mobile phones while driving. This presents obvious safety issues. However, CE devices also offer the potential to provide powerful data collection and processing – including the ability to monitor the state of the driver via heart rate, body temperature, eye glances or movement. They also vastly improve aftermarket opportunities and upgradability which can directly impact high value, low environmental impact manufacturing.
Supervisors: Professor Paul Jennings, Dr Stewart Birrell, Dr Elizabeth Crundall (JLR)
Aimee Williams - PhD Project: The comparison of the fit of models to data and how this might be used to gain better understanding or categorisation of driver behaviour with respect to fuel economy
To consider the fitting of models to data and the limits of what one might expect with regards to their performance, and what the influence of different estimation techniques might have upon this. To consider criteria for "good models". To develop formal methods to compare the performance of different models that will yield insights into the system concerned, by way of characterising the degree that the assumptions and principles utilised within the models seem to capture the behaviour observed with application to the influence of driving behaviour on fuel economy. To describe formal methods to aid in conglomerating the understanding of the effect of different possibilities of causes, in the form of classifying the degree of importance of the behaviour to different effects and the frequency with which they occur in correlation with each other for different driving behaviour and the result on fuel economy.
Supervisors: Prof Paul Jennings and Dr Andrew McGordon