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：
Georgios Giannakis
金额：
$ 95.83万
依托单位：
University of Minnesota-Twin Cities
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

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103256&HistoricalAwards=false
关键词：
CPS Medium Collaborative Research Collective

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实时情境意识，这些意识通过车辆到车辆（V2V）和车辆到网络（V2X）共享。这种方法是将传感器的感知与对其处境的解释相结合，以实现更安全的决定，并考虑到车辆和基础设施之间的通信局限性。支持自动驾驶和自动驱动车辆的高速公路系统将包括基础设施传感器和车载车载传感器，它们之间的连通性有很大的连接以及整个运输网络的分布式智能。由此产生的集体智能是一种自动驾驶汽车作为移动传感器，可与固定的基础设施传感器相互增强，以构建交通的实时图片。这张实时图片用于开发主动驾驶动作，以优化交通流量并最大程度地减少事故风险。更广泛的影响包括专注于对需要研究生培训的职业感兴趣的本科生，以扩大计算和工程领域的参与。主动驾驶的集体智能框架包括以下模块：1）场景构建，由信号处理和机器学习组成，用于从多模式多模型多视感传感器构建驾驶环境的表示； 2）情境解释，包括在渐进级别上驱动环境动态分析； 3）决策，由优化和控制组成，以支持安全驾驶和优化流动； 4）由沟通和网络科学组成的故障保护网络，该网络在象征性的传感和通信条件下支持优化的交通流，以及在受损的传感和通信条件下进行的调节流程。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛的影响来通过评估的支持，并被视为值得通过评估的支持。

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：
Vehicles
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

DOI：
10.1109/ieeeconf56349.2022.10052052
发表时间：
2022
期刊：
Asilomar Conference on Signals Systems and Computers
影响因子：
0
作者：
Traganitis, Panagiotis A.;Giannakis, Georgios B.
通讯作者：
Giannakis, Georgios B.

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

DOI：
10.1109/tits.2022.3148105
发表时间：
2022
期刊：
IEEE Transactions on Intelligent Transportation Systems
影响因子：
8.5
作者：
Hanqiu Wang;Rongqing Zhang;Xiang Cheng;Liuqing Yang
通讯作者：
Liuqing Yang

Bayesian Optimization for Task Offloading and Resource Allocation in Mobile Edge Computing

DOI：
10.1109/ieeeconf56349.2022.10051868
发表时间：
2022-10
期刊：
2022 56th Asilomar Conference on Signals, Systems, and Computers
影响因子：
0
作者：
Jiahe Yan;Qin Lu;G. Giannakis
通讯作者：
Jiahe Yan;Qin Lu;G. Giannakis

Bayesian Self-Supervised Learning Using Local and Global Graph Information

DOI：
10.1109/camsap58249.2023.10403487
发表时间：
2023-12
期刊：
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
影响因子：
0
作者：
Konstantinos D. Polyzos;A. Sadeghi;G. Giannakis
通讯作者：
Konstantinos D. Polyzos;A. Sadeghi;G. Giannakis