III: Medium: Collaborative Research: Integrating Large-Scale Machine Learning and Edge Computing for Collaborative Autonomous Vehicles

III：媒介：协作研究：集成大规模机器学习和边缘计算以实现协作自动驾驶汽车

• 批准号: 2348169
• 负责人: Heng Huang
• 金额: $ 60万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348169&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Integrating

Monitoring of possible hazards and disasters are crucial for mitigating their effects on the physical environment or to humans. The unmanned Aerial Vehicles (UAVs) have been successfully used in surveillance systems, also for many other applications such as monitoring infrastructure, vegetation growth, coastline, traffic, etc. Due to the widespread applications, a higher level of intelligence and autonomy is required to ensure safety and operational efficiency. The emerging high-resolution sensors and deep learning techniques hold great promise for autonomous UAVs. However, the unprecedented scale and complexity of sensing data (such as aerial images) have presented critical computational bottlenecks requiring new concepts and enabling tools. To address these challenges, this project focuses on designing principled large-scale machine learning, edge computing systems, energy efficient algorithms and tools that are used to achieve the real-time prediction, utilize cloud and edge computing resources, advance data-driven model-based approaches, assure the safe and agile collaborative vehicles navigation. These results address the challenges in decision support and data revolution and lead to the next generation collaborative autonomous systems.The research objective of this project is to address the computational challenges in the innovative real-time and intelligent collaborative autonomous vehicles. A novel large-scale machine learning and edge computing framework is developed to integrate the emerging key computational techniques, including fast deep learning optimizations, asynchronous federated learning, cross domain deep learning model compression, hierarchical edge computing, and collaborative autonomous aerial and ground vehicles. Unlike most existing systems that perform big data analysis in central servers or clustering for offline learning, this project provides promising new directions to the real-time analysis of high-throughput sensor data by addressing the critical embedded device data analysis issues including efficiency, scalability, distributed computing, energy saving, and space reduction. The research project combines rigorous theoretical analysis and emerging application studies, and contributes to both academic research and potential commercialized products. Such unique capabilities enable new computational applications in a large number of research areas. It advances and thus extends the relationship between engineering innovation and computational analysis.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.

监测可能发生的危害和灾害对于减轻其对自然环境或对人类的影响至关重要。无人驾驶飞行器（uav）已经成功地用于监视系统，也用于许多其他应用，如监测基础设施，植被生长，海岸线，交通等。由于广泛的应用，需要更高水平的智能和自主性，以确保安全和运行效率。新兴的高分辨率传感器和深度学习技术为自主无人机带来了巨大的希望。然而，前所未有的规模和复杂的传感数据（如航空图像）提出了关键的计算瓶颈，需要新的概念和启用工具。为了应对这些挑战，该项目专注于设计有原则的大规模机器学习、边缘计算系统、节能算法和工具，用于实现实时预测，利用云和边缘计算资源，推进基于数据驱动模型的方法，确保安全敏捷的协同车辆导航。这些结果解决了决策支持和数据革命中的挑战，并导致了下一代协作自治系统。该项目的研究目标是解决创新的实时智能协同自动驾驶汽车中的计算挑战。开发了一种新型的大规模机器学习和边缘计算框架，以集成新兴的关键计算技术，包括快速深度学习优化，异步联邦学习，跨域深度学习模型压缩，分层边缘计算以及协作自主空中和地面车辆。与大多数在中央服务器或集群中进行离线学习的大数据分析的现有系统不同，该项目通过解决关键的嵌入式设备数据分析问题，包括效率、可扩展性、分布式计算、节能和减少空间，为高通量传感器数据的实时分析提供了有希望的新方向。该研究项目结合了严谨的理论分析和新兴的应用研究，对学术研究和潜在的商业化产品都有贡献。这种独特的能力使新的计算应用在大量的研究领域。它推进并扩展了工程创新与计算分析之间的关系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。