activity
20182025
most citedProbabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network

99 citations · 248 across the 34 of their papers we have counts for

collaborators
Showing 2024 · cs.HCShow all

5 papers · 2 filters

cs.HC2024★ 5 cited

Virtual Urban Field Studies: Evaluating Urban Interaction Design Using Context-Based Interface Prototypes

Robert Dongas, Kazjon Grace, Samuel Gillespie +3

In this study, we propose the use of virtual urban field studies (VUFS) through context-based interface prototypes for evaluating the interaction design of auditory interfaces. Vir…

cs.HC2024★ 20 cited

Designing Interactions With Shared AVs in Complex Urban Mobility Scenarios

Marius Hoggenmueller, Martin Tomitsch, Stewart Worrall

In this article, we report on the design and evaluation of an external human-machine interface (eHMI) for a real autonomous vehicle (AV), developed to operate as a shared transport…

cs.HC2024★ 45 cited

Context-Based Interface Prototyping: Understanding the Effect of Prototype Representation on User Feedback

Marius Hoggenmueller, Martin Tomitsch, Luke Hespanhol +3

The rise of autonomous systems in cities, such as automated vehicles (AVs), requires new approaches for prototyping and evaluating how people interact with those systems through co…

cs.HC2024★ 4 cited

A Tangible Multi-Display Toolkit to Support the Collaborative Design Exploration of AV-Pedestrian Interfaces

Marius Hoggenmuller, Martin Tomitsch, Callum Parker +4

The advent of cyber-physical systems, such as robots and autonomous vehicles (AVs), brings new opportunities and challenges for the domain of interaction design. Though there is co…

cs.HC2024★ 25 cited

Pedestrian-Vehicle Interaction in Shared Space: Insights for Autonomous Vehicles

Yiyuan Wang, Luke Hespanhol, Stewart Worrall +1

Shared space reduces segregation between vehicles and pedestrians and encourages them to share roads without imposed traffic rules. The behaviour of road users (RUs) is then contro…