Microscopic crowd simulators rely on models of local interaction (e.g. collision avoidance) to synthesize the individual motion of each virtual agent. The quality of the resulting motions heavily depends on this component, which has significantly improved in the past few years. Recent advances have been in particular due to the introduction of a short-horizon motion prediction strategy that enables anticipated motion adaptation during local interactions among agents. However, the simplicity of prediction techniques of existing models somewhat limits their domain of validity. In this paper, our key objective is to significantly improve the quality of simulations by expanding the applicable range of motion predictions. To this end, we present a novel local interaction algorithm with a new context-aware, probabilistic motion prediction model. By context-aware, we mean that this approach allows crowd simulators to account for many factors, such as the influence of environment layouts or in-progress interactions among agents, and has the ability to simultaneously maintain several possible alternate scenarios for future motions and to cope with uncertainties on sensing and other agent's motions. Technically, this model introduces "collision probability fields" between agents, efficiently computed through the cumulative application of  Warp Operators  on a source  Intrinsic Field.  We demonstrate how this model significantly improves the quality of simulated motions in challenging scenarios, such as dense crowds and complex environments.
['David Wolinski', 'Ming C. Lin', 'Julien Pettré']
WarpDriver: context-aware probabilistic motion prediction for crowd simulation