Pith. sign in

REVIEW 2 cited by

Dynamic Group Behaviors for Interactive Crowd Simulation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1602.03623 v1 pith:TJJD43SF submitted 2016-02-11 cs.MA

Liang He , Jia Pan , Sahil Narang , Wenping Wang , Dinesh Manocha This is my paper · ORCID
classification cs.MA
keywords groupbehaviorsdynamicinteractivesimulationavoidancecoherentcollision
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We present a new algorithm to simulate dynamic group behaviors for interactive multi-agent crowd simulation. Our approach is general and makes no assumption about the environment, shape, or size of the groups. We use the least effort principle to perform coherent group navigation and present efficient inter-group and intra-group maintenance techniques. We extend the reciprocal collision avoidance scheme to perform agent-group and group-group collision avoidance that can generate collision-free as well as coherent and trajectories. The additional overhead of dynamic group simulation is relatively small. We highlight its interactive performance on complex scenarios with hundreds of agents and compare the trajectory behaviors with real-world videos.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Internal State Estimation in Groups via Active Information Gathering

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A robot with a learned crowd model and active probing planner estimates the Eysenck PEN personalities of many people at once, in simulation and small human studies.

  2. Fast Simulation of Crowd Collision Avoidance

    cs.RO 2019-08 conditional novelty 6.0 of 10

    A GPU implementation of the ORCA pedestrian steering model achieves up to 30x faster simulation than a multi-core CPU version, enabling real-time crowds above 100,000 agents.

Pith tools