1 / 18

CA-LOD: Collision Avoidance Level of Detail for Scalable, Controllable Crowds

Sébastien Paris, Anton Gerdelan , Carol O’Sullivan { Sebastien.Paris , gerdelaa , Carol.OSullivan }@ cs.tcd.ie GV2 group, Trinity College Dublin. CA-LOD: Collision Avoidance Level of Detail for Scalable, Controllable Crowds. CA-LOD Research Objectives.

lucita
Download Presentation

CA-LOD: Collision Avoidance Level of Detail for Scalable, Controllable Crowds

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sébastien Paris, Anton Gerdelan, Carol O’Sullivan {Sebastien.Paris, gerdelaa, Carol.OSullivan}@cs.tcd.ie GV2 group, Trinity College Dublin CA-LOD: Collision Avoidance Level of Detail for Scalable, Controllable Crowds

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

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

  4. 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 Rule based Particle based Motion In Games 2009

  5. Behavioural model overview Autonomous agent Interaction Environment Motion In Games 2009

  6. CA-LOD: Overview • Motion models: • LOD 0:n neighbours and topologyCollision Avoidance • LOD 1:1 neighbour CA • LOD 2:topology CA • Classic LOD distribution(camera visibility and distance) Realism Performance Motion In Games 2009

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

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

  9. 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 Motion In Games 2009

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

  11. 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 Motion In Games 2009

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

  13. Results: Performance Possible CA-LOD distribution for real time performance: Single Thread Multi-Thread (4 threads) on quad-core Motion In Games 2009

  14. Results: Demo Motion In Games 2009

  15. Conclusions • Able to simulate very large crowds (10,000) in real-time using commodity hardware • Graphical and animation optimisations • Intel Core2 Quad Q8200 2.33GHz • 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? Motion In Games 2009

  16. 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) Motion In Games 2009

  17. http://gv2.cs.tcd.ie/metropolis Online resources Acknowledgement Motion In Games 2009

  18. 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 445-455, 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

More Related