Multi Agent Visualisation and Interactive Rendering

多智能体可视化和交互式渲染

• 批准号: 2302087
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2302087
• 关键词: Multi; Agent; Visualisation; Interactive; Rendering

Addressing the fast simulation of accurate, dense crowds. This involves harnessing the GPU architecture, which has a theoretically higher computational throughput than equivalent generation CPU. The simulations are an agent-based model, which contains inherent parallelism, which is suitable for running on the GPU.The work involves examining existing models of pedestrian collision avoidance, and creating novel extensions on the GPU, that can better deal with simulating more accurate people within a dense crowd. The work examines the effects of dense crowds and the collision avoidance that occurs, and attempts to address issues that arise in high densities by construction of new simulation models.Research questions: What is the current state of simulating crowds? How true-to-life are the current models?Approach: Development of pedestrian collision avoidance models using GPU architectureKey aims: develop collision avoidance simulation models that can run for many people, and provide accurate behaviour in dense crowds. Validate developed models against real behaviour.

解决快速模拟精确密集人群的问题。这涉及到利用GPU架构，它在理论上具有比同代CPU更高的计算吞吐量。模拟是一个基于代理的模型，它包含固有的并行性，适合在GPU上运行。工作涉及检查现有的行人碰撞避免模型，并在GPU上创建新的扩展，可以更好地处理在密集人群中模拟更准确的人。这项工作研究密集人群的影响和避免碰撞发生，并试图解决的问题，在高密度的建设新的模拟models.Research问题：什么是模拟人群的当前状态？目前的模型有多逼真？方法：使用GPU架构开发行人防撞模型主要目标：开发可供多人运行的防撞仿真模型，并在密集人群中提供准确的行为。反真实的行为发展模型。