Multi Agent Visualisation and Interactive Rendering

解决了准确、密集人群的快速模拟问题。这涉及到利用GPU架构，从理论上讲，GPU架构的计算吞吐量高于同等一代的CPU。这些模拟是基于智能体的模型，包含固有的并行性，适合在GPU上运行。工作包括检查现有的行人防撞模型，并在GPU上创建新的扩展，以更好地处理在密集人群中模拟更准确的人。这项工作考察了密集人群的影响和发生的碰撞避免，并试图通过构建新的模拟模型来解决高密度情况下出现的问题。研究问题：模拟人群的当前状态是什么？当前的模型有多逼真？方法：使用GPU架构开发行人防撞模型关键目标：开发可以运行于许多人的防撞仿真模型，并在密集人群中提供准确的行为。根据实际行为验证开发的模型。

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.