Outputs (4)

Identifying interaction types and functionality for automated vehicle virtual assistants: An exploratory study using speech acts cluster analysis (2023)
Journal Article

Onboard virtual assistants with the ability to converse with users are gaining favour in supporting effective human-machine interaction to meet safe standards of operation in automated vehicles (AVs). Previous studies have highlighted the need to com... Read More about Identifying interaction types and functionality for automated vehicle virtual assistants: An exploratory study using speech acts cluster analysis.

I, AV: A Ghost Driver Field Study Exploring the Application of Anthropomorphism in AV-Pedestrian Communication (2023)
Conference Proceeding

We employed the 'Ghost Driver' methodology to emulate an autonomous vehicle (AV) and explored pedestrians' (n=520) crossing behaviour in response to external human-machine interfaces (eHMIs). Three eHMI designs were created to replace absent pedestri... Read More about I, AV: A Ghost Driver Field Study Exploring the Application of Anthropomorphism in AV-Pedestrian Communication.

Ghost Busting: A Novel On-Road Exploration of External HMIs for Autonomous Vehicles (2023)
Conference Proceeding

The absence of a human driver in future autonomous vehicles means that explicit pedestrian-driver communication is not possible. Building on the novel ‘Ghost Driver’ methodology to emulate an autonomous vehicle, we developed prototype external human-... Read More about Ghost Busting: A Novel On-Road Exploration of External HMIs for Autonomous Vehicles.

A validation study of a fixed-based, medium fidelity driving simulator for human–machine interfaces visual distraction testing (2023)
Journal Article

Studies comparing results captured in a simulator with those on road are important to validate the approach but are scarce in the context of secondary task distraction due to the potential ramifications of diverting attention away from safe driving.... Read More about A validation study of a fixed-based, medium fidelity driving simulator for human–machine interfaces visual distraction testing.