A Metaverse-Based Teaching Building Evacuation Training System With Deep Reinforcement Learning
利用元宇宙和深度强化学习技术，构建教学楼疏散训练系统，通过实时采集人群分布和灾害位置信息，动态规划最优逃生路径，提高疏散效率，实验证明优于传统静态引导方法。
With the development of IoT, virtual reality, cloud computing, and digital twin technologies, the advent of metaverse has attracted increasing world attention. Metaverse integrates and applies multiple emerging technologies to cloud education, smart health, digital government, and emergency evacuation. Evacuation systems are of great importance to ensure life safety. Due to panic, people in a building may not be able to make the right judgment to choose an optimal path to leave the building in case of an emergency event such as a fire. As a branch of machine learning, deep reinforcement learning (DRL) can model an evacuation scene, collect real-time information, such as crowd distribution and disaster location, find the optimal escape path with a path-planning algorithm, induce the movement state of the crowd through dynamic guidance signs, and improve the evacuation efficiency. In this article, we apply DRL technology to solve the efficient emergency evacuation problem with the help of metaverse and show a training system built upon metaverse that would enable evacuees to choose the most efficient route and leave the building in the least amount of time. The information collected by various sensors, such as video cameras and smoke detectors, can give a whole picture of the status of the building in a real-time manner. The collected data are processed by cloud servers in which a DRL model is trained to dynamically guide evacuees. Experiments in different simulation scenes demonstrate that the proposed method is superior to the traditional static guidance method in saving evacuation time. It can effectively avoid major crowding along the evacuation route and improve evacuation efficiency.