Collaborative Research: CPS: Medium: RUI: Cooperative AI Inferencein Vehicular Edge Networks for Advanced Driver-Assistance Systems

协作研究：CPS：中：RUI：用于高级驾驶员辅助系统的车辆边缘网络中的协作人工智能推理

• 批准号: 2128378
• 负责人: Yu Wang
• 金额: $ 34.2万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128378&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; RUI

Artificial Intelligence (AI) has shown superior performance in enhancing driving safety in advanced driver-assistance systems (ADAS). State-of-the-art deep neural networks (DNNs) achieve high accuracy at the expense of increased model complexity, which raises the computation burden of onboard processing units of vehicles for ADAS inference tasks. The primary goal of this project is to develop innovative collaborative AI inference strategies with the emerging edge computing paradigm. The strategies can adaptively adjust cooperative inference techniques for best utilizing available computation and communication resources and ultimately enable high-accuracy and real-time inference. The project will inspire greater collaborations between experts in wireless communication, edge computing, computer vision, autonomous driving testbed development, and automotive manufacturing, and facilitate AI applications in a variety of IoT systems. The educational testbed developed from this project can be integrated into courses to provide hands-on experiences. This project will benefit undergraduate, master, and Ph.D. programs and increase under-represented groups’ engagement by leveraging the existing diversity-related outreach efforts.A multi-disciplinary team with complementary expertise from Rowan University, Temple University, Stony Brook University, and Kettering University is assembled to pursue a coordinated study of collaborative AI inference. The PIs explore integrative research to enable deep learning technologies in resource-constrained ADAS for high-accuracy and real-time inference. Theory-wise, the PIs plan to take advantage of the observation that DNNs can be decomposed into a set of fine-grained components to allow distributed AI inference on both the vehicle and edge server sides for inference acceleration. Application-wise, the PIs plan to design novel DNN models which are optimized for the cooperative AI inference paradigm. Testbed-wise, a vehicle edge computing platform with V2X communication and edge computing capability will be developed at Kettering University GM Mobility Research Center. The cooperative AI inference system will be implemented, and the research findings will be validated on realistic vehicular edge computing environments thoroughly. The data, software, and educational testbeds developed from this project will be widely disseminated. Domain experts in autonomous driving testbed development, intelligent transportation systems, and automotive manufacturing will be engaged in project-related issues to ensure relevant challenges in this project are impactful for real-world applications.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.

人工智能（AI）在增强高级驾驶辅助系统（ADAS）的驾驶安全性方面表现出了上级性能。最先进的深度神经网络（DNN）以增加模型复杂性为代价实现了高精度，这增加了ADAS推理任务的车载处理单元的计算负担。该项目的主要目标是利用新兴的边缘计算范式开发创新的协作AI推理策略。该策略可以自适应地调整协作推理技术，以最好地利用可用的计算和通信资源，并最终实现高精度和实时的推理。该项目将激发无线通信、边缘计算、计算机视觉、自动驾驶测试台开发和汽车制造领域专家之间的更大合作，并促进人工智能在各种物联网系统中的应用。从这个项目开发的教育测试平台可以集成到课程中，以提供实践经验。本项目将惠及本科生、硕士生和博士生。计划，并通过利用现有的多样性相关的外展工作增加代表性不足的群体的参与。一个多学科的团队与来自罗文大学，天普大学，斯托尼布鲁克大学和凯特林大学的互补专业知识组装，以追求协作人工智能推理的协调研究。PI探索综合研究，以使深度学习技术在资源受限的ADAS中实现高精度和实时推理。从理论上讲，PI计划利用DNN可以分解为一组细粒度组件的观察结果，以允许在车辆和边缘服务器端进行分布式AI推理，从而加速推理。在应用方面，PI计划设计新的DNN模型，这些模型针对协作AI推理范式进行了优化。在测试平台方面，将在凯特林大学通用汽车移动研究中心开发具有V2X通信和边缘计算能力的车辆边缘计算平台。将实现协同人工智能推理系统，并在真实的车辆边缘计算环境中对研究结果进行全面验证。该项目开发的数据、软件和教育试验平台将得到广泛传播。自动驾驶试验台开发、智能交通系统和汽车制造领域的专家将参与项目相关问题的研究，以确保该项目中的相关挑战对现实世界的应用具有影响力。该奖项反映了NSF的法定使命，并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。