User evaluations: Evaluating how people use technology is one of the cornerstones of our work within Human Factors. This can be completed in a simulated environment (i.e. using our 3xD Simulator) or real-world studies. By understanding how people interact with and react to the technology, we can make informed decisions about design.
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Driver state monitoring: Understanding the current state of the driver by assessing physiology, biometrics, emotions and eye glance behaviours. By understanding this, we aim to infer in real-time aspects such as trust or engagement in technology. Another application is providing adaptive information to the driver via interface designs, or assessing the competence of the driver to 'takeover' from automation.
Understanding driver behaviour: Even as we move towards fully autonomous vehicles, understanding driver behaviour is central to Human Factors research. Examples of this include designing effective multi-modal warnings for automated vehicle warnings and handover requests, or understanding users' knowledge requirements during different modes of driving. With the 3xD Simulator we have a unique facility for conducting truly driver-in-the-loop evaluations.
Requirements capture: Understanding user requirements is a key factor for the development of a successful system or interface. Novel, mixed methods are used to capture what people want and subsequently convert these user requirements into system functional specifications.
Public engagement: We regularly engage with the public, both to capture requirements through focus groups, but also to disseminate our research to a wider audience, including non-expert end users.
Electric vehicles: Some of our previous research has focused on how people use and interact with Electric Vehicles (EVs), focusing on how driver behaviour can influence range of an EV and subsequent range anxiety, to parking behaviours to support Wireless Electric Vehicle Charging, and the design of EV charging points.
Outside of Automotive: We conduct research outside of the Automotive domain too. This includes design recommendations and user evaluations for an Intelligent Train seat reservation system, both for a customer facing app, but also the crew back end system.
Cooperative Autonomous Driving Systems
We aim to exploit new opportunities by enabling cooperation among agents, i.e. road users and infrastructures, and designing novel solutions that could help in addressing three key technical challenges for autonomous driving systems: positioning, perception, and control of autonomous vehicles.
A multidisciplinary challenge, we're focusing on two particular aspects:
1. Designing ultra-low-latency communications and computing platforms as enablers of cooperative autonomous driving systems. This involves various aspects of distributed computing and network 5G architecture design for highly reliable and low latency communisations
2. Designing frameworks for application in localisation, perception, and control of autonomous driving systems using a variety of techniques and tools from Information Theory, Estimation Theory, Artificial Intelligence (Machine Learning) and predictive controlWe lead or work on several national and international collaborative research projects with our industry partners
The commercialisation of highly autonomous driving solutions is constrained by technical, regulatory and economical challenges. Our focus is to exploit new opportunities in ultra-low-latency communications and application frameworks, by enabling cooperation of both road users and infrastructures, and designing novel solutions to address the positioning, perception and control of autonomous vehicles.