Towards social behavior in virtual-agent navigation

Kremyzas, Angelos; Jaklin, Norman; Gerærts, Roland

doi:10.1007/s11432-016-0074-9

Cited by 15 publications

(15 citation statements)

References 26 publications

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“…We ignore this force in our work to avoid further complexity. The classical SFM has been extended by [4], [28] to include pedestrian group interaction by introducing the group force − → 𝑓 𝑔𝑟𝑜𝑢𝑝 as:…”

Section: Social Force Model With Pedestrian Groups (Sfmg)mentioning

confidence: 99%

SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories

Hossain¹,

Johora²,

Müller³

et al. 2022

Preprint

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Autonomous robots and vehicles are expected to soon become an integral part of our environment. Unsatisfactory issues regarding interaction with existing road users, performance in mixed-traffic areas and lack of interpretable behavior remain key obstacles. To address these, we present a physics-based neural network, based on a hybrid approach combining a social force model extended by group force (SFMG) with Multi-Layer Perceptron (MLP) to predict pedestrian trajectories considering its interaction with static obstacles, other pedestrians and pedestrian groups. We quantitatively and qualitatively evaluate the model with respect to realistic prediction, prediction performance and prediction "interpretability". Initial results suggest, the model even when solely trained on a synthetic dataset, can predict realistic and interpretable trajectories with better than state-of-the-art accuracy.

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Section: Social Force Model With Pedestrian Groups (Sfmg)mentioning

confidence: 99%

SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories

Hossain¹,

Johora²,

Müller³

et al. 2022

Preprint

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“…Kremyzas et al [KJG16] focused on scenarios with dense crowds or obstacles that can (temporarily) break the coherence of a group. Their method roughly defines a group as coherent if all members can see each other and if they are all moving in the same direction.…”

Section: Other Types Of Local Behaviormentioning

confidence: 99%

Algorithms for Microscopic Crowd Simulation: Advancements in the 2010s

Toll

Pettré

2021

Computer Graphics Forum

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The real-time simulation of human crowds has many applications. Simulating how the people in a crowd move through an environment is an active and ever-growing research topic. Most research focuses on microscopic (or 'agent-based') crowdsimulation methods that model the behavior of each individual person, from which collective behavior can then emerge. This state-of-the-art report analyzes how the research on microscopic crowd simulation has advanced since the year 2010. We focus on the most popular research area within the microscopic paradigm, which is local navigation, and most notably collision avoidance between agents. We discuss the four most popular categories of algorithms in this area (force-based, velocity-based, vision-based, and data-driven) that have either emerged or grown in the last decade. We also analyze the conceptual and computational (dis)advantages of each category. Next, we extend the discussion to other types of behavior or navigation (such as group behavior and the combination with path planning), and we review work on evaluating the quality of simulations. Based on the observed advancements in the 2010s, we conclude by predicting how the research area of microscopic crowd simulation will evolve in the future. Overall, we expect a significant growth in the area of data-driven and learning-based agent navigation, and we expect an increasing number of methods that re-group multiple 'levels' of behavior into one principle. Furthermore, we observe a clear need for new ways to analyze (real or simulated) crowd behavior, which is important for quantifying the realism of a simulation and for choosing the right algorithms at the right time. CCS Concepts• Computing methodologies → Motion path planning; Real-time simulation; Intelligent agents;

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“…Collision avoidance is a popular research topic, and increasingly intelligent solutions have been proposed [Dutra et al 2017;Helbing and Molnár 1995;van den Berg et al 2011;Wolinski et al 2016]. Local planning can also entail other tasks, such as modelling social groups [Kremyzas et al 2016] and adapting an agent's motion to the local density [Best et al 2014] or flow [van Goethem et al 2015].…”

Section: Global and Local Path Planningmentioning

confidence: 99%

Connecting Global and Local Agent Navigation via Topology

Toll

Pettré

2019

Motion, Interaction and Games

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Figure 1: We define a navigation strategy as a set of decisions to pass obstacles and agents via the left (red) or right (blue). Left image: A global path yields a long-term strategy for obstacles. Middle image: A local velocity yields a short-term strategy for nearby agents and obstacles. For the obstacle marked with '!', the global and local strategies are in conflict. Right image: Our method detects these conflicts, and it allows the agent to plan a new global path that complies with its local strategy. ABSTRACTWe present a novel topology-driven method for improving the navigation of agents in virtual environments. In agent-based crowd simulations, the combination of global path planning and local collision avoidance can cause conflicts and undesired motion. These conflicts are related to the decisions to pass obstacles or agents on certain sides. In this paper, we define an agent's navigation behavior as a topological strategy amidst obstacles and other agents. We show how to extract such a strategy from a global path and from a local velocity. Next, we propose a simulation framework that computes these strategies for path planning, path following, and collision avoidance. By detecting conflicts between strategies, we can decide reliably when and how an agent should re-plan an alternative path. As such, this work bridges a long-existing gap between global and local planning. Experiments show that our method can improve the behavior of agents while preserving real-time performance. It can be applied to many agent-based simulations, regardless of their specific navigation algorithms. The strategy concept is also suitable for explicitly sending agents in particular directions.

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Towards social behavior in virtual-agent navigation

Cited by 15 publications

References 26 publications

SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories

SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories

Algorithms for Microscopic Crowd Simulation: Advancements in the 2010s

Connecting Global and Local Agent Navigation via Topology

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