Sébastien Paris, Anton Gerdelan, Carol O’Sullivan {Sebastien.Paris, gerdelaa, GV2 group, Trinity College Dublin

CA-LOD Research Objectives Optimise the main behavioural bottleneck: character motion control Specific Objectives: 1. Each crowd member is a fully autonomous agent 2. Obstacle avoidance and path planning 3. Scale crowd to bigger size by using LOD on behaviour 4. Develop a series of algorithms that scale well with LOD without losing agents’ autonomy 23/11/2009Motion In Games 20092

Related work 1/2  Level of Detail techniques Mainly graphical ○ Geometrical LODs [Lue02] ○ Impostors / Geopostors [Dob05] Behaviour seldom addressed BUT main CPU bottleneck Existing Behaviour optimisations ○ Break crowd autonomy (pre-computed paths) [Pet06] ○ Constrain environment modelling (crowd patches) [Yer09] 23/11/2009Motion In Games Crowd patches

Related work 2/2  Micro-simulation: autonomous agents with several decision layers Rational / cognitive: action selection, path planning Reactive: collision avoidance ○ Rule based algorithm [Rey99] : wall crossing / late adaptation ○ Particle based [Hel05] : only for high densities 23/11/2009Motion In Games Rule based Particle based

Behavioural model overview 23/11/2009Motion In Games Autonomous agentInteractionEnvironment

 Motion models: LOD 0: n neighbours and topology Collision Avoidance LOD 1: 1 neighbour CA LOD 2: topology CA  Classic LOD distribution (camera visibility and distance) CA-LOD: Overview 23/11/2009Motion In Games P ERFORMANCE R EALISM

LOD 2: Path Planning  Topological representation ( Delaunay triangulation )  Path planning (A*)  Visual optimisation 7

LOD 2: Path Planning  Cons: No dynamic CA Crowds tend to form into lanes along planned paths 23/11/20098  Pros: Very fast to compute (~ 60 µs / agent / s) Allows full individual autonomy

LOD 1: Fuzzy controller  Bend path around other pedestrians  Represent env. using fuzzy sets for distance and angle to nearest front neighbour  Match inputs using fuzzy inference  Aggregate outputs and modify desired speed and steering 23/11/2009Motion In Games 20099

LOD 1: Fuzzy controller  Cons: Collides when 2+ oncoming neighbours Hard to optimise rules manually 23/11/2009Motion In Games  Pros: Disperses people Smoothes CA-LOD transition Very fast – 1 neighbour (~ 190 µs / agent / s)

LOD 0: Geometric Avoider  Analyse all nearby neighbours and topology  Anticipate dynamic agents’ movement  Represent environment with a “radar” structure  Extract a solution minimising interactions and avoidance effort 23/11/2009Motion In Games

LOD 0: Geometric Avoider  Cons: Computationally expensive (~ 600 µs / agent / s) Hard to optimise decision factors manually 23/11/  Pros: Realistic collision avoidance Anticipation Effort minimisation

Results: Performance 23/11/2009Motion In Games Single ThreadMulti-Thread (4 threads) on quad-core Possible CA-LOD distribution for real time performance:

Results: Demo 23/11/2009Motion In Games

Conclusions  Able to simulate very large crowds (10,000) in real-time using commodity hardware Graphical and animation optimisations Intel Core2 Quad Q GHz GeForce 9800 GT  Overall motion convincing with current LOD algorithms but further tuning possible.  Perfect CA not desirable though! Can we handle bumps / physics realistically? 23/11/2009Motion In Games

Future Works  Optimising fuzzy rules / geometric factors with a Genetic Algorithm  Investigate a more complex fuzzy controller Multiple neighbours, Adapt to specific situations  Investigate other CA algorithms (additional LODs)  Find optimal LOD ranges (perceptual experiments)  Unified LOD (w/ graphics, animation, sound etc)  Distribute LOD based on “focus area” (salient agents / groups / zones) 23/11/2009Motion In Games

23/11/2009Motion In Games

Referenced Works [Lue02]D. Luebke et al. “Level of Detail for 3D Graphics”. Elsevier Science Inc., New York, NY, USA (2002) [Dob05]S. Dobbyn et al. “Geopostors: a real-time geometry/impostor crowd rendering system”. ACM Trans. Graph. 24(3), 933 (2005) [Yer09]B. Yersin et al. “Crowd Patches: Populating Large-Scale Virtual Environments for Real-Time Applications”, I3D’09 [Pet06]J. Pettré et al. “Real-time navigating crowds: scalable simulation and rendering”, Computer Animation and Virtual Worlds, vol. 17, CASA 2006, pp , 2006 [Rey99]C.W. Reynolds “Steering behaviors for autonomous characters”. Game Developers Conference 1999 [Hel05]D. Helbing et al. “Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions”. Transportation Science 39(1) (2005) 1–24