RINGS: Enabling Joint Sensing, Communication, and Multi-tenant Edge AI for Cooperative Perception Systems

RINGS：为协作感知系统提供联合感知、通信和多租户边缘人工智能

• 批准号: 2148353
• 负责人: Kaikai Liu
• 金额: $ 66.51万
• 依托单位: San Jose State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148353&HistoricalAwards=false
• 关键词: RINGS; Enabling; Joint; Sensing; Communication

Nationally, 40 percent of all traffic crashes involve intersections, with a quarter of all traffic deaths and about half of all injuries occurring in such locations. Recent advancements in vehicle communication and perception systems have led to significant improvement of situational awareness in modern connected and semi-autonomous vehicles. However, these light-based perception systems are limited by view blockage in busy traffic intersections and thus cannot reliably detect all objects. To improve sensing reliability, many autonomous systems rely heavily on multiple active sensors including 3D LiDAR, Radar and Ultrasound sensors. However, the improved perception reliability comes at the cost of bigger and more complex systems with multiple vendor-specific components, which cause much higher power consumption and increased maintenance, security and reliability issues. Moreover, there are no coordination or multiple access control mechanisms for nearby active sensors, thus potentially making nearby perception systems interfere with each other in busy traffic intersections. The goal of this project is to design a next generation system with multi-modal sensing and low-latency communication capabilities that share the same essential hardware including the spectrum band, baseband processing, and computing units. This design will result in a low-cost and compact perception device that provides cooperative sensing with nearby devices with improved resilience. To improve the device utilization and harness the power of multiple artificial intelligence (AI) services, this project proposes an edge computing framework to enable multi-tenant AI capabilities on the proposed perception system.The Federal Highway Administration (FHA) reports approximately 2.5 million traffic intersection accidents every year. The successful conclusion of this project will enable future sensing and communication systems to cooperate with the infrastructure and nearby devices automatically, detecting objects and mapping busy traffic intersections reliably, thus decreasing the number of accidents and fatalities. The proposed joint sensing and communication system will serve as a well-rounded, reference design for the development community to use in future connected and sensing systems. The proposed cooperative sensing mechanism will help lower the cost of individual devices while at the same time achieving higher resilience and minimizing interference between nearby systems. The proposed research will also lower barriers for undergraduate and K-12 students (especially underrepresented groups) to learn intelligent embedded systems design, internet-of-things, and AI systems while accessing state-of-the-art edge-computing lab system settings with remote testing, deployment, monitoring, orchestration, and over-the-air programming capabilities. The lessons learned and system developed in this project will serve as a plug-and-play resilient next-generation networking solution to unlock more innovative future 5G/6G projects and ideas.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.

在全国范围内，40%的交通事故涉及交叉路口，四分之一的交通死亡和大约一半的受伤发生在这些地点。车辆通信和感知系统的最新进展已经导致现代连接和半自动车辆中的态势感知的显著改善。然而，这些基于光的感知系统受限于忙碌交通路口中的视野阻挡，因此不能可靠地检测所有对象。为了提高传感可靠性，许多自主系统严重依赖多个有源传感器，包括3D激光雷达、雷达和超声波传感器。然而，感知可靠性的提高是以具有多个供应商特定组件的更大和更复杂的系统为代价的，这会导致更高的功耗和增加的维护，安全性和可靠性问题。此外，对于附近的有源传感器没有协调或多个访问控制机制，从而潜在地使得附近的感知系统在忙碌的交通路口中彼此干扰。该项目的目标是设计一个具有多模式传感和低延迟通信功能的下一代系统，该系统共享相同的基本硬件，包括频谱带，基带处理和计算单元。这种设计将导致低成本和紧凑的感知设备，其提供与附近设备的协作感测，具有改进的弹性。为了提高设备利用率并利用多种人工智能（AI）服务的功能，该项目提出了一个边缘计算框架，以在拟议的感知系统上启用多租户AI功能。联邦公路管理局（FHA）每年报告约250万起交通交叉口事故。该项目的成功完成将使未来的传感和通信系统能够自动与基础设施和附近的设备合作，检测物体并可靠地绘制忙碌的交通路口，从而减少事故和死亡人数。拟议的联合传感和通信系统将作为开发社区在未来连接和传感系统中使用的全面参考设计。所提出的协作感测机制将有助于降低单个设备的成本，同时实现更高的弹性并最大限度地减少附近系统之间的干扰。拟议的研究还将降低本科生和K-12学生（特别是代表性不足的群体）学习智能嵌入式系统设计，物联网和人工智能系统的障碍，同时通过远程测试，部署，监控，编排和空中编程功能访问最先进的边缘计算实验室系统设置。该项目中的经验教训和开发的系统将作为即插即用的弹性下一代网络解决方案，以释放更多创新的未来5G/6 G项目和想法。该奖项反映了NSF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。