CPS: Medium: Collaborative Research: Collective Intelligence for Proactive Autonomous Driving (CI-PAD)

CPS:中:协作研究:主动自动驾驶集体智慧 (CI-PAD)

基本信息

  • 批准号:
    2103256
  • 负责人:
  • 金额:
    $ 95.83万
  • 依托单位:
  • 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:
    2020-10-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

The aim of this project is to develop real-time situational awareness that is shared via vehicle-to-vehicle (V2V) and vehicle-to-network (V2X). The approach is to combine the perception of sensors with interpretation of their situation to enable safer decisions, and take into account the limitations of the communication between vehicles and infrastructure. A highway system that supports autonomous and self-driven vehicles will include infrastructure sensors and onboard vehicle sensors, with massive connectivity among them and distributed intelligence across the entire transportation network. The resulting collective intelligence is one where autonomous vehicles serve as mobile sensors that augment one another along with fixed infrastructure sensors, to construct a real-time picture of traffic. This real-time picture is used to develop proactive driving actions that optimize traffic flow and minimize accident risk. The broader impacts include focused mentoring of undergraduate students who are interested in careers that require graduate training, to broaden participation in the fields of computing and engineering.The researchers organize an interdisciplinary project in signal processing and machine learning, control and optimization, communication and network science. The collective intelligence framework for proactive driving includes the following modules: 1) Scene Construction, consisting of signal processing and machine learning for constructing a representation of the driving environment from multi-modal multi-view sensors; 2) Situational Interpretation, consisting of driving environment dynamic analysis at progressive levels; 3) Decision Making, consisting of optimization and control to support proactive driving for safety and optimized flow; and 4) A Failsafe Network, consisting of communication and network science that supports optimized traffic flow under nominal conditions of sensing and communication, and moderated flow under conditions of compromised sensing and communication.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.
该项目的目的是开发通过车对车(V2 V)和车对网(V2X)共享的实时态势感知。该方法是将联合收割机的感知与对它们的情况的解释相结合,以实现更安全的决策,并考虑到车辆和基础设施之间的通信限制。支持自动驾驶和自驾车的高速公路系统将包括基础设施传感器和车载传感器,它们之间具有大规模的连接性,并在整个交通网络中提供分布式智能。由此产生的集体智慧是一种自动驾驶汽车作为移动的传感器,与固定基础设施传感器沿着相互增强,以构建实时交通状况。这种实时图像用于制定主动驾驶行动,以优化交通流量并最大限度地降低事故风险。更广泛的影响包括对那些对需要研究生培训的职业感兴趣的本科生进行重点指导,以扩大对计算和工程领域的参与。研究人员组织了一个跨学科项目,涉及信号处理和机器学习,控制和优化,通信和网络科学。主动驾驶的集体智能框架包括以下模块:1)场景构建,包括信号处理和机器学习,用于从多模态多视图传感器构建驾驶环境的表示; 2)情景解释,包括渐进水平的驾驶环境动态分析; 3)决策,包括优化和控制,以支持安全和优化流程的主动驾驶;以及4)故障安全网络,由通信和网络科学组成,支持在传感和通信的标称条件下优化的业务流,该奖项反映了NSF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。

项目成果

期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Priority-Based Autonomous Intersection Management (AIM) Scheme for Connected Automated Vehicles (CAVs)
  • DOI:
    10.3390/vehicles3030032
  • 发表时间:
    2021-08
  • 期刊:
  • 影响因子:
    2.2
  • 作者:
    Hui Zhang;Rongqing Zhang;Chen Chen-Chen;Dongliang Duan;Xiang Cheng;Liuqing Yang
  • 通讯作者:
    Hui Zhang;Rongqing Zhang;Chen Chen-Chen;Dongliang Duan;Xiang Cheng;Liuqing Yang
Identifying Dependent Annotators in Crowdsourcing
识别众包中的依赖注释器
Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network
基于时空图卷积网络的分层交通流预测
Integrated Distributed Wireless Sensing with Over-The-Air Federated Learning
集成分布式无线传感与空中联合学习
Bayesian Optimization for Task Offloading and Resource Allocation in Mobile Edge Computing
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Georgios Giannakis其他文献

Georgios Giannakis的其他文献

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{{ truncateString('Georgios Giannakis', 18)}}的其他基金

Collaborative Research: ECCS-CCSS Core: Resonant-Beam based Optical-Wireless Communication
合作研究:ECCS-CCSS核心:基于谐振光束的光无线通信
  • 批准号:
    2332173
  • 财政年份:
    2024
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
Collaborative Research: CIF: Medium: Robust Learning over Graphs
协作研究:CIF:媒介:图上的鲁棒学习
  • 批准号:
    2312547
  • 财政年份:
    2023
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Continuing Grant
IMR: MM-1C: Learning-driven Models for 5G Internet Measurements
IMR:MM-1C:5G 互联网测量的学习驱动模型
  • 批准号:
    2220292
  • 财政年份:
    2022
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
Collaborative Research: SWIFT: Cognitive-IoV with Simultaneous Sensing and Communications via Dynamic RF Front End
合作研究:SWIFT:通过动态射频前端实现同步传感和通信的认知车联网
  • 批准号:
    2128593
  • 财政年份:
    2021
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
CCSS: Online Learning for IoT Monitoring and Management
CCSS:物联网监控和管理在线学习
  • 批准号:
    2126052
  • 财政年份:
    2021
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
Hybrid mmWave mMIMO Transceiver Design for Doubly-Selective Channels
适用于双选通道的混合毫米波 mMIMO 收发器设计
  • 批准号:
    2102312
  • 财政年份:
    2020
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
CIF: Medium: Adaptive Diffusions for Scalable and Robust Learning over Graphs
CIF:中:用于图上可扩展和鲁棒学习的自适应扩散
  • 批准号:
    1901134
  • 财政年份:
    2019
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
CCSS: Collaborative Research: Learn-and-Adapt to Manage Dynamic Cyber-Physical Networks
CCSS:协作研究:学习和适应管理动态信息物理网络
  • 批准号:
    1711471
  • 财政年份:
    2017
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
CCSS: Collaborative Research: Smart-Grid Powered Green Communications in Heterogeneous Networks
CCSS:协作研究:异构网络中智能电网驱动的绿色通信
  • 批准号:
    1508993
  • 财政年份:
    2015
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant
EAGER-DynamicData: Judicious Censoring, Random Sketching, and Efficient Validate for Learning Patterns from Dynamically-Changing and Large-Scale Data Sets
EAGER-DynamicData:明智的审查、随机草图和高效验证,用于从动态变化的大规模数据集中学习模式
  • 批准号:
    1500713
  • 财政年份:
    2015
  • 资助金额:
    $ 95.83万
  • 项目类别:
    Standard Grant

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