CPS: Medium: Collaborative Research: Active Shooter Tracking & Evacuation Routing for Survival (ASTERS)

美国大多数学前12年级的学区都投入了大量资源来防范活跃的枪手，例如，学校加固、社区规划、可疑行为的识别、执法的危机培训，以及为学生、教师和所有学校工作人员进行的培训演习。然而，当这样的主动射击活动正在进行时，学生和学校工作人员只能以指令的形式提供模糊的指导，如“逃跑-隐藏-战斗”协议。主动射击跟踪和逃生疏散路线(ASTS)项目将通过多个摄像头和麦克风实时跟踪枪手，计算每个学生、教师和教职员工到达安全的最佳疏散路径，并通过与学生、家长、教育工作者和管理人员以及学校资源官员和学校安全官员组成的互联社区共同创建的移动应用程序界面来传达这些信息，从而补充这些努力。Asters将结合多模式传感、机器学习和信号处理技术，在保护学校社区成员隐私的同时，准确定位持枪歹徒和武器。它还将使用新的计算机视觉和高性能计算解决方案来估计人群密度和人员移动，以及新的优化和实时模拟算法，以根据建筑物布局和预测的枪手移动来预测理想的疏散路线。ASTER将与学校合作，使用数字模拟数据和现实生活中的练习相结合的方式，开发一个带注释的、多模式的主动射击数据集。研究团队还将与急救人员合作，确保ASTER与他们的需求保持一致。通过移动应用程序向每一组平民提供定制和可操作的命令，可能会极大地提高安全疏散的机会。信息将提供明确的可操作的信息和建议，例如“Shooter正在离开自助餐厅前往健身房。你最好的出口是正门外”，而不是让个人惊慌失措地判断。此外，ASTER将能够自动和即时地向911呼叫中心报告枪手的位置和物理属性以及正在使用的武器类型。这将为作出反应的巡逻员提供规划战术进攻的重要战略信息，并减轻对不可靠目击者陈述的依赖，如果不能克服的话。之前发生的大规模枪击事件的数据表明，为人们提供有关疏散的准确信息和指导非常重要。ASTER项目将实现智能安全系统，集成传感器、通信、算法和人为因素研究，为脆弱人群提供救生信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Most preK-12 school districts in the United States dedicate significant resources to safeguard against active shooters, e.g., school hardening, community planning, identification of suspicious behavior, crisis training for law enforcement, and training exercises for students, teachers, and all school personnel. However, when such an active-shooting event is in progress, only vague guidance is available to students and school personnel in the form of directives such as the "run-hide-fight" protocol. The Active Shooter Tracking and Evacuation Routing for Survival (ASTERS) project will complement these efforts by tracking a shooter in real time across multiple cameras and microphones, calculate the optimum evacuation path to safety for each student, teacher, and staff member, and communicate this information through a mobile app interface that is co-created in partnership with a connected community of students, parents, educators and administrators as well as school resource officers and school safety officers. ASTERS will incorporate multi-modal sensing, machine learning and signal processing techniques to accurately localize a gunman and weapons while preserving privacy of school community members. It will also use new computer vision and high-performance computing solutions to estimate crowd density and movement of people, and novel optimization and real-time simulation algorithms to predict ideal evacuation routes based on the building layout and predicted movement of the shooter. ASTERS will collaborate with schools to develop an annotated, multi-modal active shooter data set using a combination of digital simulation data and real-life practice drills. The research team will also partner with first-responders to ensure that ASTERS aligns with their needs. Providing customized and actionable commands to each group of civilians through a mobile app will potentially vastly improve chances of safe evacuation. Messages will provide clear actionable information and suggestions, such as "Shooter is leaving the cafeteria heading to the gym. Your best exit is out the Main Entrance", rather than leave it up to individuals' panicked judgement. Moreover, ASTERS will enable automated and instantaneous reporting of location and physical attributes of shooter and type of weapons being used, to a 911 call center. This will provide responding patrol officers with critical strategic information for planning a tactical offensive and alleviate, if not overcome, the dependence on unreliable eye-witness accounts. Data from previous mass shootings demonstrate the important of providing people accurate information and guidance about evacuation. The ASTERS project will enable the realization of smart safety systems that integrate sensors, communication, algorithms, and human factors research to provide life-saving information to vulnerable people.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.