Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Group Behaviors for Systems with Significant Dynamics
Autonomous Robots
Modeling Individual Behaviors in Crowd Simulation
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
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
ACM SIGGRAPH 2006 Papers
Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions
Transportation Science
Controlling individual agents in high-density crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Evaluating motion graphs for character animation
ACM Transactions on Graphics (TOG)
Real-time navigation of independent agents using adaptive roadmaps
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
Interactive navigation of multiple agents in crowded environments
Proceedings of the 2008 symposium on Interactive 3D graphics and games
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
Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation)
Egocentric affordance fields in pedestrian steering
Proceedings of the 2009 symposium on Interactive 3D graphics and games
SteerBug: an interactive framework for specifying and detecting steering behaviors
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
SteerBench: a benchmark suite for evaluating steering behaviors
Computer Animation and Virtual Worlds - International Workshop Motion in Games (MIG08)
An Open Framework for Developing, Evaluating, and Sharing Steering Algorithms
MIG '09 Proceedings of the 2nd International Workshop on Motion in Games
PLEdestrians: a least-effort approach to crowd simulation
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
A modular framework for adaptive agent-based steering
I3D '11 Symposium on Interactive 3D Graphics and Games
Footstep navigation for dynamic crowds
I3D '11 Symposium on Interactive 3D Graphics and Games
Improved benchmarking for steering algorithms
MIG'11 Proceedings of the 4th international conference on Motion in Games
Towards a quantitative approach for comparing crowds
Computer Animation and Virtual Worlds
A statistical similarity measure for aggregate crowd dynamics
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Multi-domain real-time planning in dynamic environments
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Context-sensitive data-driven crowd simulation
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
Hi-index | 0.00 |
Navigation and steering in complex dynamically changing environments is a challenging research problem, and a fundamental aspect of immersive virtual worlds. While there exist a wide variety of approaches for navigation and steering, there is no definitive solution for evaluating and analyzing steering algorithms. Evaluating a steering algorithm involves two major challenges: (a) characterizing and generating the space of possible scenarios that the algorithm must solve, and (b) defining evaluation criteria (metrics) and applying them to the solution. In this paper, we address both of these challenges. First, we characterize and analyze the complete space of steering scenarios that an agent may encounter in dynamic situations. Then, we propose the representative scenario space and a sampling method that can generate subsets of the representative space with good statistical properties. We also propose a new set of metrics and a statistically robust approach to determining the coverage and the quality of a steering algorithm in this space. We demonstrate the effectiveness of our approach on three state of the art techniques. Our results show that these methods can only solve 60% of the scenarios in the representative scenario space.