Human Factors research primarily lies in applying a scientifically rigorous, mixed methods approach to understanding users’ interactions with technology, systems or services within simulated and real-world environments. Put simply, we aim to better understand how people interact with 'stuff', and how this can lead to the design of better end products. One such application, which is the current focus of much of our research, surrounds Connected and Autonomous Vehicles (CAV - connected to the internet or other road users, and/or self-driving). This is an emerging field of research, both from a technological perspective (e.g. sensor fusion, Swarm Intelligence etc.) but also in terms of our social understanding (e.g. trust in automation, public acceptance etc.).
Our research interests and capabilities include:
- 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.
- 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.
RACeD - Research for Advanced Concept Development; WMG / JLR EngD Programme
Funder: Jaguar Land Rover
SWARM - Self-organising Wide area Autonomous vehicle Real-time Marshalling
LSAT User Evaluations
Funder: JLR, under subcontract via the UK Autodrive project (Innovate UK)
CloseR - Customer Loyalty and Seat Reservation for Intelligent Trains
See this link for a full list of the Human Factors in Intelligent Vehicles publications, or to the right for recent publications.
Alongside project delivery we strive to be at the forefront of our teaching activities, with the Human Factors team leading and contributing to Undergraduate, Postgraduate and Professional programme teaching. This includes leading the School of Engineering 4th Year module, Design for Vehicle Comfort, and contributing significantly to WMGs new MSc in Smart, Connected and Autonomous Vehicles (SCAV) for the core module of Human Technology Interaction.
Recent Journal Publications
Geitner C, Birrell S, Krehl C, Jennings P. 2018. A haptic pedal assistant system: Influence of shoe type, age, and gender on the perception of haptic pulses. Human Factors, 60, 496-509.
Khastgir S, Birrell S, Dhadyalla, G, Sivencrona H and Jennings P. 2017. Towards Increased Reliability by Objectification of Hazard Analysis and Risk Assessment (HARA) of Automated Automotive Systems. Safety Science, 99, 166-177.