Context-sensitive Personal Space for Dense Crowd Simulation
Bibliographic record
Abstract
Real-time simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. Existing particle-based methods assume and aim for collision-free trajectories. That is an ideal -yet not overly realistic- expectation, as near-collisions increase in dense and rushed settings compared to typically sparse pedestrian scenarios. This paper presents a method that evaluates the immediate personal space area surrounding each entity to inform its pathing decisions. While personal spaces have traditionally been modeled as having fixed radii, they actually often change in response to the surrounding context. For instance, in cases of congestion, entities tend to share more of their personal space than they normally would, simply out of necessity (e.g. leaving a concert or boarding a train). Likewise, entities travelling at higher speeds (e.g. strolling, running) tend to expect a larger area ahead of them to be their personal space. We illustrate how our agent-based method for local dynamics can reproduce several key emergent dense crowd phenomena; and how it can be efficiently computed on consumer-grade graphics (GPU) hardware, achieving interactive frame rates for simulating thousands of crowd entities in the scene.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".