Simulating Crowds and Autonomous Vehicles
This work addresses the need for efficient large-scale simulations to understand human-AV interactions, though it is incremental as it focuses on optimization of existing simulation methods.
The authors tackled the problem of simulating interactions between people and autonomous vehicles in shared urban spaces by developing a GPU-accelerated model, achieving up to 30 times faster performance than a multi-core CPU equivalent.
Understanding how people view and interact with autonomous vehicles is important to guide future directions of research. One such way of aiding understanding is through simulations of virtual environments involving people and autonomous vehicles. We present a simulation model that incorporates people and autonomous vehicles in a shared urban space. The model is able to simulate many thousands of people and vehicles in real-time. This is achieved by use of GPU hardware, and through a novel linear program solver optimized for large numbers of problems on the GPU. The model is up to 30 times faster than the equivalent multi-core CPU model.