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Interactive multi-agent simulation algorithms are used to compute thetrajectories and behaviors of different entities in virtual reality scenarios.However, current methods involve considerable parameter tweaking to generateplausible behaviors. We introduce a novel approach (Heter-Sim) that combinesphysics-based simulation methods with data-driven techniques using anoptimization-based formulation. Our approach is general and can simulateheterogeneous agents corresponding to human crowds, traffic, vehicles, orcombinations of different agents with varying dynamics. We estimate motionstates from real-world datasets that include information about position,velocity, and control direction. Our optimization algorithm considers severalconstraints, including velocity continuity, collision avoidance, attraction,and direction control. To accelerate the computations, we reduce the searchspace for both collision avoidance and optimal solution computation. Heter-Simcan simulate tens or hundreds of agents at interactive rates and we compare itsaccuracy with real-world datasets and prior algorithms. We also perform userstudies that evaluate the plausible behaviors generated by our algorithm and auser study that evaluates the plausibility of our algorithm via VR.

Heter-Sim: Heterogeneous multi-agent systems simulation by interactive data-driven optimization