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)

• 批准号: 1932139
• 负责人: Dongliang Duan
• 金额: $ 23.62万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932139&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Collective

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。一个支持自动驾驶和自动驾驶车辆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

Societal Intelligence for Safer and Smarter Transportation

• DOI: 10.1109/jiot.2021.3057131
• 发表时间: 2021-02
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Xiang Cheng;Dongliang Duan;Liuqing Yang;N. Zheng

Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking

• DOI: 10.1109/wcnc45663.2020.9120552
• 发表时间: 2020-05
• 期刊: 2020 IEEE Wireless Communications and Networking Conference (WCNC)
• 作者: Wang Pi;Pengtao Yang;Dongliang Duan;Chen Chen-Chen;Xiang Cheng;Liuqing Yang

Malicious User Detection for Cooperative Mobility Tracking in Autonomous Driving

• DOI: 10.1109/jiot.2020.2973661
• 发表时间: 2020-02
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Wang Pi;Pengtao Yang;Dongliang Duan;Chen Chen-Chen;Xiang Cheng;Liuqing Yang;Hang Li

Decomposed Iterative Optimal Power Flow with Automatic Regionalization

自动分区的分解迭代最优潮流

• DOI: 10.3390/en13184987
• 发表时间: 2020
• 期刊: Energies
• 影响因子: 3.2
• 作者: Zheng, Xinhu;Duan, Dongliang;Yang, Liuqing;Wang, Haonan
• 通讯作者: Wang, Haonan

Environmental Sensitivity Evaluation of Neural Networks in Unmanned Vehicle Perception Module

• DOI: 10.1109/wcnc45663.2020.9120722
• 发表时间: 2020-05
• 期刊: 2020 IEEE Wireless Communications and Networking Conference (WCNC)
• 作者: Yuru Li;Dongliang Duan;Chen Chen-Chen;Xiang Cheng;Liuqing Yang