Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven Optimization

Interactive multi-agent simulation algorithms are used to compute the trajectories and behaviors of different entities in virtual reality scenarios. However, current methods involve considerable parameter tweaking to generate plausible behaviors. We introduce a novel approach (Heter-Sim) that combines physics-based simulation methods with data-driven techniques using an optimization-based formulation. Our approach is general and can simulate heterogeneous agents corresponding to human crowds, traffic, vehicles, or combinations of different agents with varying dynamics. We estimate motion states from real-world datasets that include information about position, velocity, and control direction. Our optimization algorithm considers several constraints, including velocity continuity, collision avoidance, attraction, direction control. Other constraints are implemented by introducing a novel energy function to control the motions of heterogeneous agents. To accelerate the computations, we reduce the search space for both collision avoidance and optimal solution computation. Heter-Sim can simulate tens or hundreds of agents at interactive rates and we compare its accuracy with real-world datasets and prior algorithms. We also perform user studies that evaluate the plausible behaviors generated by our algorithm and a user study that evaluates the plausibility of our algorithm via VR.

Automated summary

The study introduces a novel multi-agent simulation algorithm that combines physics-based simulation and data-driven techniques to generate plausible behaviors for different types of agents. Motion states of agents are estimated from real-world datasets, and an optimization algorithm considers multiple constraints to control agent behaviors. The algorithm can efficiently simulate large numbers of agents and has been validated for plausibility through user studies.