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: 2128350
• 负责人: Haibin Ling
• 金额: $ 29.54万
• 依托单位: SUNY at Stony Brook
• 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=2128350&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）在增强高级驾驶员辅助系统（ADAS）的驾驶安全方面表现出色。最先进的深度神经网络（DNNS）以增加模型复杂性为代价实现了很高的精度，这增加了用于ADAS推理任务的车辆车载工具的计算。该项目的主要目标是通过新兴的边缘计算范式制定创新的协作AI推断策略。这些策略可以适应合作推理技术，以最佳利用可用的计算和通信资源，并最终实现高临界性和实时推理。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的专家之间的更多合作，并支持各种物联网系统中的AI应用程序。该项目开发的教育测试床可以集成到课程中，以提供动手体验。该项目将使本科，硕士和博士学位受益。通过利用现有与多样性相关的外展工作来计划和增加代表性不足的团体的参与。一个多学科的团队，拥有罗恩大学，坦普尔大学，斯托尼·布鲁克大学和Kettering大学的完整专业知识，以寻求协调的协作AI推论研究。 PIS探索了综合研究，以使资源受限的ADA中的深度学习技术用于高临界性和实时推断。从理论上讲，PIS计划利用可以将DNN分解为一组细粒组件的观察，以允许对车辆和边缘服务器侧的分布AI推断以进行推理加速。在应用方面，PIS计划设计新型DNN模型，该模型已针对合作AI推论范式进行了优化。在测试床上，将在Kettering University GM Mobility Research Center开发具有V2X通信和边缘计算能力的车辆边缘计算平台。将实施合作AI推理系统，研究结果将在现实的车辆边缘计算环境中进行验证。该项目开发的数据，软件和教育测试台将被广泛传播。自动驾驶测试床开发，智能运输系统和汽车制造业领域的领域专家将参与与项目有关的问题，以确保该项目的相关挑战对现实世界应用有影响。该奖项反映了NSF的法定任务，并通过评估该基金会的智力功能和广泛的影响来评估NSF的法定任务，并被认为是珍贵的支持。

项目成果

Transparent Object Tracking with Enhanced Fusion Module

• DOI: 10.1109/iros55552.2023.10341597
• 发表时间: 2023-09
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Kalyan Garigapati;Erik Blasch;Jie Wei;Haibin Ling

GTCaR: Graph Transformer for Camera Re-localization

• DOI: 10.1007/978-3-031-20080-9_14
• 发表时间: 2022
• 作者: Xinyi Li;Haibin Ling

ARCHIE++ : A Cloud-enabled Framework for Conducting AR System Testing in the Wild

ARCHIE：用于在野外进行 AR 系统测试的云支持框架

• DOI: 10.1109/tvcg.2022.3141029
• 发表时间: 2022
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Lehman, Sarah;Elezovikj, Semir;Ling, Haibin;Tan, Chiu
• 通讯作者: Tan, Chiu

Backdoor Cleansing with Unlabeled Data

• DOI: 10.1109/cvpr52729.2023.01176
• 发表时间: 2022-11
• 期刊: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Lu Pang;Tao Sun;Haibin Ling;Chao Chen