Pedestrian Group Behavior in a Cellular Automaton

Seitz, Michael; Köster, Gerta; Pfaffinger, Alexander

doi:10.1007/978-3-319-02447-9_67

Cited by 24 publications

(30 citation statements)

References 10 publications

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“…The problem of formally modeling motion of people walking in groups has received increasing attention in recent years, especially for developing crowd simulation tools [15], [16], [17] and mass event planning [18], [19]. Detecting groups of people is a common task in video surveillance [20] and tracking [21], [3], [22] applications.…”

Section: B Group Detection Modeling and Predictionmentioning

confidence: 99%

“…Considering group information in the motion model is known to improve the quality of people tracking [3], [22]. Common techniques for modeling group motion include imposing attraction forces to other group members [16], [22], [19], to the geometrical center of the group [2] or to the group's leader [18], or imposing a certain relative formation in which members of the group are assumed to be moving [15], [17]. An extension of the social force model that uses group information was proposed by Moussaïd et al [2].…”

Section: B Group Detection Modeling and Predictionmentioning

confidence: 99%

“…A few methods for crowd simulation assume a better understanding of the map, e.g. using gradients indicating the most direct way towards the point of interest [19], or simply use a wave-front method to determine the shortest path to the goal [18]. These methods however operate on a per-cell basis, following certain update rules for translating the pedestrians to neighboring cells, and therefore lack the expressive representation of continuous position and velocity of agents in the social force-based models.…”

Section: B Group Detection Modeling and Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Human Motion Prediction Under Social Grouping Constraints

Rudenko

Palmieri

Lilienthal

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Accurate long-term prediction of human motion in populated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety of factors including the person's intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members' motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics.

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Section: B Group Detection Modeling and Predictionmentioning

confidence: 99%

Section: B Group Detection Modeling and Predictionmentioning

confidence: 99%

Section: B Group Detection Modeling and Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Human Motion Prediction Under Social Grouping Constraints

Rudenko

Palmieri

Lilienthal

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Most real-world pedestrian contexts do not involve entire populations of individuals operating independently, so one facet of the research in this area has examined group behavior and formation to reflect crowd composition more accurately (7,8,(15)(16)(17)(18). Some pedestrian research has examined the emergence and composition of group structures through individual behaviors (19,20).…”

Section: Pedestrian Evacuationmentioning

confidence: 99%

“…Reduced to physics equations, much of the sociobehavioral decision making that influences individual members of groups within a crowd is lost and leads to oversimplification of the evacuation scenario. Attempting to fill this gap, contemporary pedestrian research and modeling have focused on agent-based or cellular automata modeling techniques that incorporate individual decision making within a group context (1,(5)(6)(7)(8).…”

mentioning

confidence: 99%

Panic That Spreads Sociobehavioral Contagion in Pedestrian Evacuations

Elzie

Frydenlund

Collins

et al. 2016

Transportation Research Record: Journal of the Transportation R

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Crowds are a part of everyday public life, from stadiums and arenas to school hallways. Occasionally, pushing within the crowd spontaneously escalates to crushing behavior, resulting in injuries and even death. The rarity and unpredictability of these incidents provides few options to collect data for research on the prediction and prevention of hazardous emergent behaviors in crowds. This study takes a close look at the way states of agitation, such as panic, can spread through crowds. Group composition-mainly family groups composed of members with differing mobility levels-plays an important role in the spread of agitation through the crowd, ultimately affecting the exit density and evacuation clearance time of a simulated venue. This study used an agent-based model of pedestrian movement during the egress of a hypothetical room and adopted an emotional, cognitive, and social framework to explore the transference and dissipation of agitation through a crowd. The preliminary results reveal that average group size in a crowd is a primary contributor to the exit density and evacuation clearance time. The study provides the groundwork on which to build more elaborate models that incorporate sociobehavioral aspects to simulate human movement during panic situations and account for the potential for dangerous behavior to emerge in crowds.

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