A crowd simulator which creates autonomous characters’ behaviour in crowds consists many components such as pathfinding, collision avoidance, character creation, behaviour system, and level of details. The majority of these involve different level of decision making in order to simulate autonomous agents’ behaviour. Some components have a few different algorithms that can be adopted. For a simulator with a large number of autonomous agents, these components need to be efficient to contribute to the creation of a faster and cheaper game environment. Otherwise bottlenecks may occur and this can led to a poor representation.

In this paper we investigate these areas, discuss and compare existing approaches in each component, and select the best combination on Xbox 360 through a series of experiments on our crowd simulator within the Microsoft XNA framework. We used the Xbox 360 console for accurate testing which is not affected by other processes running in the background. We also optimise the application to overcome bottleneck issues. Our simulator is able to handle a large number of automonous agents with a healthy frame rate of 60 FPS. Based on our implementation and testing results, some recommendations are provided in this paper, which will be useful for independent game developers who create games containing autonomous crowd for Xbox 360 using XNA framework.