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

在全国范围内，40%的交通事故涉及十字路口，四分之一的交通死亡和大约一半的伤害发生在这些地方。车辆通信和感知系统的最新进展大大提高了现代联网和半自动车辆的态势感知能力。然而，这些基于光的感知系统受到繁忙交通路口视野阻塞的限制，因此无法可靠地检测到所有物体。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更大、更复杂的系统为代价的，这些系统包含多个特定于供应商的组件，这会导致更高的功耗，并增加维护、安全性和可靠性问题。此外，附近有源传感器没有协调或多重访问控制机制，从而可能使附近感知系统在繁忙的交通路口相互干扰。该项目的目标是设计具有多模态传感和低延迟通信能力的下一代系统，这些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。这种设计将导致一种低成本和紧凑的感知设备，提供与附近设备的协作传感，提高弹性。为了提高设备利用率并利用多种人工智能（AI）服务的力量，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。美国联邦公路管理局（FHA）每年报告大约250万起交通事故。该项目的成功完成将使未来的传感和通信系统能够与基础设施和附近设备自动合作，可靠地探测物体并绘制繁忙的交通路口的地图，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一个全面的参考设计，供发展界在未来的连接和传感系统中使用。所提出的协同传感机制将有助于降低单个设备的成本，同时实现更高的弹性和最小化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，同时访问具有远程测试、部署、监控、编排和无线编程功能的最先进的边缘计算实验室系统设置。在该项目中汲取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，以开启更多创新的未来5G/6G项目和想法。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。

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.