1 research outputs found
Scenario Generalization of Data-driven Imitation Models in Crowd Simulation
Crowd simulation, the study of the movement of multiple agents in complex
environments, presents a unique application domain for machine learning. One
challenge in crowd simulation is to imitate the movement of expert agents in
highly dense crowds. An imitation model could substitute an expert agent if the
model behaves as good as the expert. This will bring many exciting
applications. However, we believe no prior studies have considered the critical
question of how training data and training methods affect imitators when these
models are applied to novel scenarios. In this work, a general imitation model
is represented by applying either the Behavior Cloning (BC) training method or
a more sophisticated Generative Adversarial Imitation Learning (GAIL) method,
on three typical types of data domains: standard benchmarks for evaluating
crowd models, random sampling of state-action pairs, and egocentric scenarios
that capture local interactions. Simulated results suggest that (i) simpler
training methods are overall better than more complex training methods, (ii)
training samples with diverse agent-agent and agent-obstacle interactions are
beneficial for reducing collisions when the trained models are applied to new
scenarios. We additionally evaluated our models in their ability to imitate
real world crowd trajectories observed from surveillance videos. Our findings
indicate that models trained on representative scenarios generalize to new,
unseen situations observed in real human crowds.Comment: 12 pages, MIG 2019 - ACM SIGGRAPH Conference on Motion, Interaction
and Game