# Fast Simulation of Crowd Collision Avoidance

arXiv: https://arxiv.org/abs/1908.10107 · HTML: https://mlsanity.com/paper/1908.10107/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments](https://mlsanity.com/paper/2402.06297/) (2024)

## Abstract (arXiv)

Real-time large-scale crowd simulations with realistic behavior, are important for many application areas. On CPUs, the ORCA pedestrian steering model is often used for agent-based pedestrian simulations. This paper introduces a technique for running the ORCA pedestrian steering model on the GPU. Performance improvements of up to 30 times greater than a multi-core CPU model are demonstrated. This improvement is achieved through a specialized linear program solver on the GPU and spatial partitioning of information sharing. This allows over 100,000 people to be simulated in real time (60 frames per second).