Researcher

Dr Rowan Hughes

Field of Research (FoR)

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Biography

Rowan Hughes grew up in Galway, Ireland, an altogether wonderful place (if a bit wet). Having a PhD in Computer Science from Trinity College Dublin and experience in the VFX business, Rowan is a veteran computer scientist and researcher with a passion for computer graphics, simulation, data science and Star Wars. He’s on twitter @rowanthughes

 

Location

3D Visualisation Aesthetics Lab - Level 4 F Block

Map reference (Google map)

Contact

0413416968

Videos

We present a novel algorithm to model density-dependent behaviours in crowd simulation. Previous work has shown that density is a key factor in governing how pedestrians adapt their behaviour. This paper specifically examines, through analysis of real pedestrian data, how density effects how agents control their rate of change of bearing angle with respect to one another. We extend upon existing synthe vision based approaches to local collision avoidance and generate pedestrian trajectories that more faithfully represent how real people avoid each other. Our approach is capable of producing realistic human behaviours, particularly in dense, complex scenarios where the amount of time for agents to make decisions is limited.
All approaches to simulating human collision avoidance for virtual crowds make simplifications to the underlying behaviour. One of the prevalent simplifications is to ignore it's holonomic aspect (i.e. sidestepping, walking backwards). This does not, however, capture the full range of how humans avoid collisions. In real world scenarios we can often observe people sidestepping around each other and obstacles in their environment. In this paper we present a new holonomic collision avoidance algorithm for real-time crowd simulation. Our model is elaborated from experimental data, which allowed us to both observe the conditions under which holonomic interactions occur, as well as the strategies walkers use during such interactions to avoid collision. Our model is general enough to be used with other collision avoidance techniques. We validate our approach by reproducing situations from our experiments and we demonstrate several examples in which our method provides more plausible collision avoidance behaviour.
DAVIS: Density-Adaptive Synthetic-Vision Based Steering for Virtual Crowds
Holonomic Collision Avoidance for Virtual Crowds