Date of this Version
Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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.
© ACM 2011. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM/Eurographics Symposium on Computer Animation, 2011, http://dx.doi.org/10.1145/2019406.2019414.
Kapadia, M., Wang, M., Singh, S., Reinman, G., & Faloutsos, P. (2011). Scenario Space: Characterizing Coverage, Quality, and Failure of Steering Algorithms. Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 53-62. http://dx.doi.org/10.1145/2019406.2019414
Date Posted: 13 January 2016