SteerBench: a benchmark suite for evaluating steering behaviors Export

@article{citeulike:5004728, abstract = {Steering is a challenging task, required by nearly all agents in virtual worlds. There is a large and growing number of approaches for steering, and it is becoming increasingly important to ask a fundamental question: how can we objectively compare steering algorithms? To our knowledge, there is no standard way of evaluating or comparing the quality of steering solutions. This paper presents SteerBench: a benchmark framework for objectively evaluating steering behaviors for virtual agents. We propose a diverse set of test cases, metrics of evaluation, and a scoring method that can be used to compare different steering algorithms. Our framework can be easily customized by a user to evaluate specific behaviors and new test cases. We demonstrate our benchmark process on two example steering algorithms, showing the insight gained from our metrics. We hope that this framework can grow into a standard for steering evaluation. Copyright {\copyright} 2009 John Wiley \& Sons, Ltd.}, author = {Singh, Shawn and Kapadia, Mubbasir and Faloutsos, Petros and Reinman, Glenn}, citeulike-article-id = {5004728}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/cav.277}, citeulike-linkout-1 = {http://www3.interscience.wiley.com/cgi-bin/abstract/122212368/ABSTRACT}, doi = {10.1002/cav.277}, journal = {Computer Animation and Virtual Worlds}, keywords = {ai, autonomous, crowd, flock, games, group, pedestrian, steering, traffic, vehicle}, number = {9999}, pages = {n/a+}, posted-at = {2009-06-29 01:52:58}, priority = {2}, title = {SteerBench: a benchmark suite for evaluating steering behaviors}, url = {http://dx.doi.org/10.1002/cav.277}, volume = {9999}, year = {2009} }

TY - JOUR ID - citeulike:5004728 L3 - citeulike-article-id:5004728 N2 - Steering is a challenging task, required by nearly all agents in virtual worlds. There is a large and growing number of approaches for steering, and it is becoming increasingly important to ask a fundamental question: how can we objectively compare steering algorithms? To our knowledge, there is no standard way of evaluating or comparing the quality of steering solutions. This paper presents SteerBench: a benchmark framework for objectively evaluating steering behaviors for virtual agents. We propose a diverse set of test cases, metrics of evaluation, and a scoring method that can be used to compare different steering algorithms. Our framework can be easily customized by a user to evaluate specific behaviors and new test cases. We demonstrate our benchmark process on two example steering algorithms, showing the insight gained from our metrics. We hope that this framework can grow into a standard for steering evaluation. Copyright © 2009 John Wiley & Sons, Ltd. IS - 9999 JF - Computer Animation and Virtual Worlds TI - SteerBench: a benchmark suite for evaluating steering behaviors VL - 9999 SP - n/a KW - ai KW - autonomous KW - crowd KW - flock KW - games KW - group KW - pedestrian KW - steering KW - traffic KW - vehicle AU - Singh, Shawn AU - Kapadia, Mubbasir AU - Faloutsos, Petros AU - Reinman, Glenn PY - 2009/// UR - http://dx.doi.org/10.1002/cav.277 ER -