Simulating Crowds and Autonomous Vehicles

TL;DR

This paper introduces a GPU-accelerated simulation model for large-scale urban environments involving people and autonomous vehicles, enabling real-time analysis of interactions to inform future research directions.

Contribution

The paper presents a novel GPU-based simulation framework that efficiently models thousands of pedestrians and autonomous vehicles in real-time, outperforming CPU-based methods.

Findings

Simulation runs up to 30 times faster than CPU models

Enables real-time analysis of large-scale urban interactions

Supports research on human-autonomous vehicle interactions

Abstract

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.