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) 的驾驶安全性方面表现出了卓越的性能，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，从而提高了驾驶安全性。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人工智能推理策略，这些策略可以自适应地调整协作推理技术，以充分利用可用的计算和通信资源。最终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。该项目开发的测试平台可以集成到课程中，以提供实践经验，该项目将使本科生、硕士和博士课程受益，并通过利用现有的与多样性相关的外展工作来提高代表性不足的群体的参与度。具有互补专业知识的多学科团队罗文大学、天普大学、石溪大学和凯特林大学联合开展协作人工智能推理研究，探索综合研究，以在资源有限的 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，以实现推理加速。在应用方面，PI 计划设计针对协作人工智能推理范例进行优化的新型 DNN 模型；在测试平台方面，凯特林大学通用汽车移动合作研究中心将开发具有 V2X 通信和边缘计算功能的车辆边缘计算平台。人工智能推理系统将得到实施，研究成果将在现实的车辆边缘计算环境中得到彻底验证，该项目开发的数据、软件和教育测试平台将广泛传播给自动驾驶测试平台开发领域的专家。智能交通系统和汽车制造将参与项目相关问题，以确保该项目中的相关挑战对实际应用产生影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，认为值得支持以及更广泛的影响审查标准。

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