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

美国大多数学前教育学区都投入了大量资源来防范活跃的枪手，例如，学校强化、社区规划、识别可疑行为、执法危机培训以及学生、教师和所有学校工作人员的培训演习。然而，当这样的主动射击事件正在进行时，学生和学校工作人员只能以“跑-躲-打”协议等指令的形式获得模糊的指导。主动射击者跟踪和疏散路线生存（ASTERS）项目将通过多个摄像头和麦克风真实的时间跟踪射击者来补充这些努力，计算每个学生，教师和工作人员的最佳疏散路径安全，并通过移动的应用程序界面传达此信息，该界面是与学生，家长，教育工作者和行政人员以及学校资源干事和学校安全干事。ASTERS将结合多模态传感、机器学习和信号处理技术，以准确定位枪手和武器，同时保护学校社区成员的隐私。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，以根据建筑布局和预测的射手移动来预测理想的疏散路线。ASTERS将与学校合作，使用数字模拟数据和现实生活中的实践演练相结合，开发一个带注释的多模态主动射击数据集。研究小组还将与第一反应者合作，以确保ASTERS符合他们的需求。通过一个移动的应用程序向每个平民群体提供定制的和可操作的命令，将有可能大大提高安全疏散的机会。消息将提供明确的可操作信息和建议，例如“射手正在离开自助餐厅前往健身房。你最好的出口是从正门出去”，而不是让个人恐慌地判断。此外，ASTERS将能够自动和即时地向911呼叫中心报告射手的位置和物理属性以及所使用的武器类型。这将为作出反应的巡逻人员提供关键的战略信息，以规划战术进攻，并减轻（如果不能克服）对不可靠的目击者证词的依赖。以往大规模枪击事件的数据表明，为人们提供准确的疏散信息和指导非常重要。ASTERS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，为弱势群体提供救生信息。该奖项反映了NSF的法定使命，通过使用基金会的智力价值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.