ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation. (21st September 2017)

• Record Type: Journal Article
• Authors: Luo, Linbo; Chai, Cheng; Ma, Jianfeng; Zhou, Suiping; Cai, Wentong
• Is Part Of: Computer graphics forum. Volume 37:Number 1(2018)
• Page Start: 375
• Page End: 388
• Publication Date: 2017-09-21
• Subjects: behavioural animation -- animation -- human simulation -- animation -- motion planning -- animation -- Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: 3D Graphics and Realism—Animation, I.6.5 [Simulation and Modelling]: Model Development—Modelling methodologies; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13303
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels. Abstract : How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics.