Priority driven Local Optimization for Crowd Simulation

We provide an initial model and preliminary findings of a lookahead based local optimization scheme for collision resolution between agents in large goal-directed crowd simulations. Considering crowd simulation to be a global optimization problem, we break down this large problem into smaller problems where each potential collision resolution step is independently optimized in terms of a criticality measure. Agents resolved earlier in order of criticality, maintain the optimized velocity obtained, for the resolution of agents that come later in that order. Hence, the problem is converted to a low dimensional optimization problem of one or two agents where all other obstacles are static or deterministically dynamic. We illustrate the performance of our method on four well known test scenarios.