Generating routes for entities in virtual environments, such as simulated vehicles or synthetic human characters, is a long-standing problem, and route planning algorithms have been developed and studied for some time. Existing route planning algorithms, including the widely used A*algorithm, are generally intended to achieve optimality in some metric, such as minimum length or minimum time. Comparatively little attention has been given to route realism, defined as the similarity of the algorithm-generated route to the route followed by real humans in the same terrain with the same constraints and goals. Commercial game engines have seen increasing use as a context for research. To study route realism in a game engine, two developments were needed: a quantitative metric for measuring route realism and a game engine able to capture route data needed to compute the realism metric. Enhancements for recording route data for both synthetic characters and human players were implemented within the Unreal Tournament 2004 game engine. A methodology for assessing the realism of routes and other behaviors using a quantitative metric was developed. The enhanced Unreal Tournament 2004 game engine and the realism assessment methodology were tested by capturing data required to calculate a metric of route realism.
Engineering Route Planning Algorithms
