Understanding Intelligence
Intuitive Crowd Behaviour in Dense Urban Environments using Local Laws
TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
Transportation Science
Real-time navigation of independent agents using adaptive roadmaps
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
Crowd Simulation
Being a part of the crowd: towards validating VR crowds using presence
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Egocentric affordance fields in pedestrian steering
Proceedings of the 2009 symposium on Interactive 3D graphics and games
SteerBench: a benchmark suite for evaluating steering behaviors
Computer Animation and Virtual Worlds - International Workshop Motion in Games (MIG08)
Analysis of an efficient rule-based motion planning system for simulating human crowds
The Visual Computer: International Journal of Computer Graphics - Special Issue on Cypberworlds'2009
A synthetic-vision based steering approach for crowd simulation
ACM SIGGRAPH 2010 papers
PLEdestrians: a least-effort approach to crowd simulation
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Real-time density-based crowd simulation
Computer Animation and Virtual Worlds
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In this paper, we propose a new approach to modeling natural steering behaviors of virtual humans. We suspect that a small number of steering strategies are sufficient for generating typical pedestrian behaviors observed in daily-life situations. Through these limited strategies we show that complex steering behaviors are generated by executing appropriate steering strategies at the appropriate time. In our model, decisions on the selection, scheduling and execution of steering strategies in a given situation are based on the matching results between the currently perceived spatial-temporal patterns and the prototypical cases in an agent's experience base. From a modeler's point of view, our approach is intuitive to use. Our model is carefully evaluated through a three-stage validation process, using experimental studies on basic test scenarios, model comparisons under standard but more complex test scenarios, and sensitivity analysis on key model parameters. Experimental results show that our model is able to generate results that reflect the collective efficiency of crowd dynamics and is in agreement with existing literature on pedestrian studies.