Explicit Energy-Minimal Short-Term Path Planning for Collision Avoidance in Crowd Simulation

Abstract

In traditional crowd simulation methods, global path planning (GPP) and local collision avoidance (LCA) were mostly used to advance pedestrians toward their own goals without colliding. However, we found that using those methods in bidirectional flows can force a pedestrian to get stuck among the incoming people, walk through the congestion, or even unintentionally occupy in a dense area, although more comfortable passageway exists. These odd behaviors are usually produced and simply noticeable in bidirectional case. This paper aims at reducing these artifacts to achieve more behavioral fidelity, by adding the explicit metabolic-energy-minimal short-term path planning (MEM) in between GPP and LCA. For energy analysis, the optimal control theory with the objective energy function from the study of biomechanics was employed, which finally leads to the useful optimal walking characteristics for the pedestrians. The simulation results show that the pedestrians with MEM can adapt their moving to avoid the congestion, resulting in more promising lane changing and overtaking behaviors. Even though MEM was mainly developed to deal with the artifacts in bidirectional flows, it can be extended with a little modification and can produce significant behavioral improvement for multi-directional case as shown in the last part of the paper.