CyberTraining: Implementation: Small: Collaborative and Integrated Training on Connected and Autonomous Vehicles Cyber Infrastructure

网络培训：实施：小型：联网和自动驾驶车辆网络基础设施的协作和综合培训

• 批准号: 2017564
• 负责人: Song Fu
• 金额: $ 49.94万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017564&HistoricalAwards=false
• 关键词: CyberTraining; Implementation; Small; Collaborative; Integrated

In response to the quick development and adoption of connected and autonomous vehicles (CAVs), U.S. cities and states have recently started building CAV cyberinfrastructure. However, there is not an adequate supply of skilled research workforce in this field. A key obstacle to such workforce development is the lack of well-structured training programs for utilizing CAV cyberinfrastructure to enable and potentially transform fundamental CAV research. To address this issue, a project-oriented training program will be developed in this CyberTraining project to enable scientific research workforce development for CAV cyberinfrastructure. It is estimated that the adoption of CAVs would lead to nearly $800 billion in annual social and economic benefits by 2050, therefore, it is important for the nation to invest in CAV cyberinfrastructure research workforce training programs. The proposed training program targets students and early-stage researchers who are interested in CAVs, including participants with a broad diversity in academic level and in experience level with CAVs. It is expected that more than 100 trainees will participate every year in the training program, including researchers from various domains such as cyber-physical systems, edge computing, wireless networking, deep learning, computer vision, and big data. A longstanding collaboration with the trainees and/or their advisors will be built to ensure a broad adoption of CAV cyberinfrastructure by the research community to catalyze major research advances. The long-term goal of this project is to develop a first of its kind open CAV cyberinfrastructure, an integrated training and research hub, to accelerate research and education in CAVs.The goal of this project is to develop a collaborative and integrated training program to enable scientific research work force development for Connected and Autonomous Vehicle CyberInfrastructure (CAV-CI) and foster broad adoption of CAV-CI to advance fundamental CAV related research. To achieve these goals, the project will leverage existing partnerships with relevant stakeholders to create tailored, high-impact, engaging, collaborative, and integrated training modules for CAV-CI research workforce development. With the aim of enhancing trainees design and implementation capabilities, problem-solving skills, and critical thinking ability, the proposed training program will result in: (1) a project-oriented short course plus long-term coaching and support, (2) hands-on training modules on the perception, network, and application layers in CAV-CI, (3) an annual research workshop that disseminates research results and receives feedback on the training program from the research and industrial communities, and (4) research projects for students supported through NSF's Research Experiences for Undergraduates (REU)program and capstone projects for senior undergraduates. During the training workshops, project-oriented training will be offered to actively engage trainees in learning and solving real-world problems. Three sample research projects will be designed, allowing trainees to develop complete research skills, i.e., competency to solve authentic problems. Following every sample research project, with each having a strong practical relevance and meaningfulness, two versions of training modules will be developed to reach a broader trainee group: a fundamental training module for undergraduate students and community college educators, and a research-intensive training module for graduate students and postdocs. By taking either the fundamental or the research-intensive training modules, trainees will enhance their problem-solving skills, improve their creative and independent thinking ability, as well as gaining enthusiasm and confidence in conducting CAV-CI enabled research. The CAV-CI education, research and training activities include specific goals to train individuals from underrepresented groups and the broader STEM workforce.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.

为了应对互联自动驾驶汽车 (CAV) 的快速发展和采用，美国各城市和州最近开始建设 CAV 网络基础设施。然而，该领域缺乏足够的熟练研究人员。这种劳动力发展的一个主要障碍是缺乏结构良好的培训计划来利用 CAV 网络基础设施来实现和潜在地改变 CAV 基础研究。为了解决这个问题，该网络培训项目将开发一个以项目为导向的培训计划，以促进 CAV 网络基础设施的科研人员发展。据估计，到 2050 年，CAV 的采用每年将带来近 8000 亿美元的社会和经济效益，因此，国家投资 CAV 网络基础设施研究人员培训计划非常重要。拟议的培训计划针对对 CAV 感兴趣的学生和早期研究人员，包括具有广泛多样性的学术水平和 CAV 经验水平的参与者。预计每年将有超过100名学员参加培训项目，其中包括来自信息物理系统、边缘计算、无线网络、深度学习、计算机视觉、大数据等各个领域的研究人员。将与学员和/或其顾问建立长期合作，以确保研究界广泛采用 CAV 网络基础设施，以促进重大研究进展。该项目的长期目标是开发第一个开放式 CAV 网络基础设施，一个综合培训和研究中心，以加速 CAV 的研究和教育。该项目的目标是开发一个协作和综合培训计划促进互联和自动驾驶汽车网络基础设施 (CAV-CI) 的科学研究队伍发展，并促进 CAV-CI 的广泛采用，以推进 CAV 相关的基础研究。为了实现这些目标，该项目将利用与相关利益相关者的现有合作伙伴关系，为 CAV-CI 研究人员的发展创建定制的、高影响力的、参与性的、协作性的综合培训模块。为了提高学员的设计和实施能力、解决问题的能力和批判性思维能力，拟议的培训计划将导致：（1）以项目为导向的短期课程加上长期的辅导和支持，（2）动手实践- CAV-CI 中感知、网络和应用层的培训模块，(3) 年度研究研讨会，传播研究成果并接收研究和工业界对培训计划的反馈，以及 (4) 研究项目通过 NSF 研究经验支持的学生本科生 (REU) 计划和高年级本科生顶点项目。培训期间，将提供项目导向的培训，让学员积极参与学习和解决现实问题。将设计三个样本研究项目，让学员培养完整的研究技能，即解决真实问题的能力。每个样本研究项目都具有很强的实际相关性和意义，将开发两个版本的培训模块，以覆盖更广泛的受训者群体：针对本科生和社区大学教育工作者的基础培训模块，以及研究密集型培训模块适合研究生和博士后。通过基础或研究密集型培训模块，学员将提高解决问题的能力，提高创造性和独立思考能力，并获得进行 CAV-CI 支持的研究的热情和信心。 CAV-CI 教育、研究和培训活动包括培训来自代表性不足群体的个人和更广泛的 STEM 劳动力的具体目标。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Distributed Data-Sharing Consensus in Cooperative Perception of Autonomous Vehicles

自动驾驶车辆协同感知中的分布式数据共享共识

• DOI: 10.1109/icdcs54860.2022.00119
• 发表时间: 2022-07
• 期刊: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS
• 作者: Qiu, Chenxi;Yadav, Sourabh;Squicciarini, Anna;Yang, Qing;Fu, Song;Zhao, Juanjuan;Xu, Chengzhong
• 通讯作者: Xu, Chengzhong

LiDAR-based Cooperative Relative Localization

基于LiDAR的协同相对定位

• DOI: 10.1109/iv55152.2023.10186549
• 发表时间: 2023-06
• 期刊: 2023 IEEE Intelligent Vehicles Symposium (IV
• 作者: Dong, Jiqian;Chen, Qi;Qu, Deyuan;Lu, Hongsheng;Ganlath, Akila;Yang, Qing;Chen, Sikai;Labi, Samuel
• 通讯作者: Labi, Samuel

OASD: An Open Approach to Self-Driving Vehicle

OASD：自动驾驶汽车的开放方法

• DOI: 10.1109/metrocad51599.2021.00017
• 发表时间: 2021-04
• 期刊: 2021 IEEE International Conference on Connected and Autonomous Driving
• 作者: Dhakal, Sudip;Qu, Deyuan;Carrillo, Dominic;Yang, Qing;Fu, Song
• 通讯作者: Fu, Song

Privacy-Preserving Object Detection with Secure Convolutional Neural Networks for Vehicular Edge Computing

使用用于车辆边缘计算的安全卷积神经网络进行隐私保护对象检测

• DOI: 10.3390/fi14110316
• 发表时间: 2022-11
• 期刊: Future Internet
• 影响因子: 3.4
• 作者: Bai, Tianyu;Fu, Song;Yang, Qing
• 通讯作者: Yang, Qing

VECFrame: A Vehicular Edge Computing Framework for Connected Autonomous Vehicles

VECFrame：用于联网自动驾驶车辆的车辆边缘计算框架

• DOI: 10.1109/edge53862.2021.00019
• 发表时间: 2021-09
• 期刊: 2021 IEEE International Conference on Edge Computing (EDGE
• 作者: Tang, Sihai;Chen, Bruce;Iwen, Harold;Hirsch, Jason;Fu, Song;Yang, Qing;Palacharla, Paparao;Wang, Nannan;Wang, Xi;Shi, Weisong
• 通讯作者: Shi, Weisong