EAGER: A Cloud-assisted Framework for Improving Pedestrian Safety in Urban Communities using Crowd-sourced Mobile and Wearable Device Data

EAGER：使用众包移动和可穿戴设备数据改善城市社区行人安全的云辅助框架

• 批准号: 1829066
• 负责人: Murtuza Jadliwala
• 金额: $ 12.96万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-19 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829066&HistoricalAwards=false
• 关键词: EAGER; Cloud; assisted; Framework; Improving

Pedestrian safety continues to be a significant concern in urban communities. Several recent reports indicate that injuries and fatalities in pedestrian-related accidents are steadily rising and that pedestrian distraction is one of the leading causes in such accidents. Existing systems and techniques for improving pedestrian safety, which primarily operate on users' smartphones and mobile devices in a stand-alone fashion, have several design drawbacks and performance and usability concerns that have precluded their successful adoption and usage. The goal of this project is to improve pedestrian safety by designing accurate, efficient and usable tools and techniques, which can be easily adopted by urban users. In order to accomplish this goal, this project plans to pursue a focused research agenda involving novel technologies and several exploratory and untested ideas. As part of the proposed pedestrian safety framework, accurate and energy-efficient on-device distraction detection techniques will be developed by employing multi-sensor and heterogeneous data available from upcoming mobile and wearable devices. In this direction, supervised and semi-supervised learning will be used to design efficient activity classification and distraction prediction techniques which will be empirically evaluated using proof-of-concept implementations. Unlike existing stand-alone approaches, the proposed framework employs a connected-community approach to accurately capture the impact of both a pedestrian's own actions, as well as the actions of others, on his/her safety. This involves the design and implementation of a privacy-preserving and cloud-assisted data-analytics engine to capture, analyze and notify pedestrians of impending hazardous situations from the crowd-sourced distraction data obtained from participating users. Finally, a comprehensive performance and usability evaluation will be conducted by deploying a large-scale testbed involving participants from Wichita State University's (WSU) campus community. The project outcomes, including the planned testbed, will have a significant impact on improving pedestrian safety within the WSU campus community. If successful, similar trials at an urban or city-wide scale can also be envisioned. In addition to improving pedestrian safety, this project will educate users and participants on the impact of technology on pedestrian safety and its role in improving the same. Project outcomes and results will be disseminated by means of peer-reviewed publications, white papers and open-source applications. Applications and anonymous data collected from the planned testbed will be appropriately disseminated to facilitate additional research and advances in the area of pedestrian safety technology.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.

行人安全仍然是城市社区的重大关注点。最近的几份报告表明，与行人有关的事故的伤害和死亡人数稳步上升，行人分心是此类事故中的主要原因之一。现有的系统和技术用于改善行人安全，主要以独立的方式运行用户的智能手机和移动设备，具有几个设计缺点，性能和可用性问题，这些问题无法成功采用和使用。该项目的目的是通过设计准确，高效且可用的工具和技术来改善行人安全，这可以被城市用户轻松采用。为了实现这一目标，该项目计划追求涉及新技术以及几种探索性和未经测试思想的重点研究议程。作为拟议的行人安全框架的一部分，将通过使用即将到来的移动和可穿戴设备可用的多传感器和异质数据来开发准确且节能的分散注意力分散注意力检测技术。在这个方向上，被监督和半监督的学习将用于设计有效的活动分类和分散注意力预测技术，这些技术将使用概念概念证明实现进行经验评估。与现有的独立方法不同，拟议的框架采用了连接的社区方法来准确捕获行人自己的行动以及他人对他/她的安全的影响。这涉及设计和实施隐私保护和云辅助的数据分析引擎，以捕获，分析和通知行人从参与用户获得的众筹分散注意力数据中遇到的危险情况。最后，将通过部署涉及威奇托州立大学（WSU）校园社区的参与者的大规模测试床来进行全面的绩效和可用性评估。 包括计划的测试床在内的项目成果将对改善WSU校园社区内的行人安全产生重大影响。如果成功，也可以设想在城市或全市范围内进行类似的试验。除了提高行人安全外，该项目还将教育用户和参与者有关技术对行人安全的影响及其在改进同一安全方面的作用。项目成果和结果将通过同行评审的出版物，白皮书和开源应用程序来传播。从计划的测试台收集的应用和匿名数据将得到适当传播，以促进行人安全技术领域的其他研究和进步。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响审查标准来评估的。

项目成果

Towards a Practical Pedestrian Distraction Detection Framework using Wearables

• DOI: 10.1109/percomw.2018.8480238
• 发表时间: 2017-10
• 期刊: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
• 作者: Nisha Vinayaga-Sureshkanth;Anindya Maiti;Murtuza Jadliwala;Kirsten Crager;Jibo He;Heena Rathore

A Practical Framework for Preventing Distracted Pedestrian-Related Incidents Using Wrist Wearables

• DOI: 10.1109/access.2018.2884669
• 发表时间: 2018-11
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Nisha Vinayaga-Sureshkanth;Anindya Maiti;Murtuza Jadliwala;Kirsten Crager;Jibo He;Heena Rathore