Fast Simulation of Crowd Collision Avoidance
This work addresses the need for efficient crowd simulations in applications like gaming or urban planning, but it is incremental as it adapts an existing model to a new hardware platform.
The paper tackled the problem of real-time large-scale crowd simulation by implementing the ORCA pedestrian steering model on the GPU, achieving performance improvements of up to 30 times faster than a multi-core CPU model and enabling simulation of over 100,000 people at 60 frames per second.
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).