CPS: Medium: Collaborative Research: Robust Sensing and Learning for Autonomous Driving Against Perceptual Illusion
CPS:中:协作研究:针对自动驾驶对抗知觉错觉的鲁棒感知和学习
基本信息
- 批准号:2235231
- 负责人:
- 金额:$ 70万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-07-01 至 2026-06-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Autonomous driving is on the verge of revolutionizing the transportation system and significantly improving the well-being of people. An autonomous vehicle relies on multiple sensors and AI algorithms to facilitate sensing and perception for navigating the world. As the automotive industry primarily focuses on increasing autonomy levels and enhancing perception performance in mainly benign environments, the security and safety of perception technologies against physical attacks have yet to be thoroughly investigated. Specifically, adversaries creating physical-world perceptual illusions may pose a significant threat to the sensing and learning systems of autonomous vehicles, potentially undermining trust in these systems. This research project aims to deepen our understanding of the security and safety risks under physical attacks. The project endeavors to bolster sensing and learning resilience in autonomous driving against malicious perceptual illusion attacks. The success of the project will significantly advance the security and safety of autonomous driving in the face of emerging physical-world threats, paving the way for the safe deployment of autonomous vehicles in next-generation transportation systems.The goal of this project is to investigate advanced sensing and learning technologies to enhance the precision and robustness of autonomous driving in intricate and hostile environments. The team’s approach includes: (i) a comprehensive framework to evaluate key vulnerabilities in software/hardware components of autonomous driving systems and devise effective attack vectors for generating false and deceptive perceptions; (ii) a real-time super-resolution radar sensing technology and a data fusion approach that integrates features from various sensor types at both the middle and late stages to effectively bolster the robustness of each sensing modality against illusions; and (iii) a systematic framework to enhance the algorithmic generality and achieve robust perception against multi-modal attacks using multi-view representation learning. The presented solutions will undergo rigorous testing using simulations and experiments to validate their effectiveness and robustness. These solutions contribute to the development of more secure and robust autonomous driving systems, capable of withstanding perceptual illusion attacks in real-world scenarios. The project will also offer research training opportunities for underrepresented students across diverse levels and age groups. The resulting novel technology will be shared as open-source for broader dissemination and advancement of the knowledge developed through this project.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.
自主驾驶即将彻底改变运输系统,并显着改善人们的福祉。自动驾驶汽车依靠多个传感器和AI算法来促进世界导航的感知和感知。随着汽车行业的主要重点是提高自治水平并提高主要是良性环境中的感知表现,因此感知技术免受身体攻击的安全性和安全性尚待进行彻底研究。具体而言,产生物理世界感知幻觉的对手可能会对自动驾驶汽车的敏感性和学习系统构成重大威胁,从而可能破坏对这些系统的信任。该研究项目旨在加深我们对身体攻击下的安全和安全风险的理解。该项目努力支持敏感性和学习弹性,以自主驱动恶意感知幻觉攻击。面对新兴的身体世界威胁,该项目的成功将显着提高自主驾驶的安全性和安全性,为在下一代运输系统中安全部署自动驾驶汽车的安全铺平道路。该项目的目的是研究高级敏感性和学习技术,以增强自动驾驶在复杂环境中自动驾驶的精确性和鲁棒性。该团队的方法包括:(i)评估自动驾驶系统软件/硬件组件中的关键漏洞的综合框架,并设计有效的攻击向量来产生虚假和欺骗性的看法; (ii)一种实时的超分辨率雷达灵敏度技术和一种数据融合方法,该方法在中间和晚期都整合了来自各种传感器类型的特征,以有效地增强每种灵敏度方式对幻觉的鲁棒性; (iii)一个系统的框架,可增强算法通用性,并使用多视图表示学习实现对多模式攻击的强大感知。提出的解决方案将使用模拟和实验进行严格的测试,以验证其有效性和鲁棒性。这些解决方案有助于开发更安全和强大的自主驾驶系统,能够在现实世界中承受感知幻觉攻击。该项目还将为跨潜水员级别和年龄段的人数不足的学生提供研究培训机会。由此产生的新技术将被共享为开放源代码,以更广泛的传播和通过该项目开发的知识的进步。该奖项反映了NSF的法定任务,并使用基金会的知识分子优点和更广泛的影响审查标准,通过评估被认为是珍贵的支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Qiben Yan其他文献
PeerClean: Unveiling peer-to-peer botnets through dynamic group behavior analysis
- DOI:
10.1109/infocom.2015.7218396 - 发表时间:
2015-01-01 - 期刊:
- 影响因子:0
- 作者:
Qiben Yan;Yao Zheng;Hou, Y. Thomas - 通讯作者:
Hou, Y. Thomas
Osprey: A fast and accurate patch presence test framework for binaries
Osprey:快速准确的二进制补丁存在测试框架
- DOI:
10.1016/j.comcom.2021.03.011 - 发表时间:
2021-03 - 期刊:
- 影响因子:6
- 作者:
Peiyuan Sun;Qiben Yan;Haoyi Zhou(通讯);Jianxin Li - 通讯作者:
Jianxin Li
SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation
SpecView:具有奇异频谱转换的恶意软件频谱可视化框架
- DOI:
10.1109/tifs.2021.3124725 - 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Jian Yu;Yuewang He;Qiben Yan;Xiangui Kang - 通讯作者:
Xiangui Kang
Security Enhanced Communications in Cognitive Networks
- DOI:
- 发表时间:
2014-08 - 期刊:
- 影响因子:0
- 作者:
Qiben Yan - 通讯作者:
Qiben Yan
Privacy-Preserving and Residential Context-Aware Online Learning for IoT-Enabled Energy Saving with Big Data Support in Smart Home Environment
隐私保护和住宅情境感知在线学习,通过智能家居环境中的大数据支持实现物联网节能
- DOI:
10.1109/jiot.2019.2903341 - 发表时间:
2019 - 期刊:
- 影响因子:10.6
- 作者:
Pan Zhou;Guohui Zhong;Menglan Hu;Ruixuan Li;Qiben Yan;Kun Wang;Shouling Ji;Dapeng Wu - 通讯作者:
Dapeng Wu
Qiben Yan的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Qiben Yan', 18)}}的其他基金
SaTC: CORE: Small: Robust Speaker and Speech Recognition Under AI-Driven Physical and Digital Attacks
SaTC:核心:小型:人工智能驱动的物理和数字攻击下的鲁棒扬声器和语音识别
- 批准号:
2310207 - 财政年份:2023
- 资助金额:
$ 70万 - 项目类别:
Continuing Grant
NeTS: Small: Collaborative Research: Cooperative Interference-Embracing Communication in Multi-Hop Wireless Networks
NeTS:小型:协作研究:多跳无线网络中的协作抗干扰通信
- 批准号:
1949753 - 财政年份:2019
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
SaTC: CORE: Small: URadio: Towards Secure Smart Home IoT Communication Using Hybrid Ultrasonic-RF Radio
SaTC:CORE:小型:URadio:使用混合超声波射频无线电实现安全的智能家居物联网通信
- 批准号:
1950171 - 财政年份:2019
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
SaTC: CORE: Small: URadio: Towards Secure Smart Home IoT Communication Using Hybrid Ultrasonic-RF Radio
SaTC:CORE:小型:URadio:使用混合超声波射频无线电实现安全的智能家居物联网通信
- 批准号:
1817029 - 财政年份:2018
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
NeTS: Small: Collaborative Research: Cooperative Interference-Embracing Communication in Multi-Hop Wireless Networks
NeTS:小型:协作研究:多跳无线网络中的协作抗干扰通信
- 批准号:
1717898 - 财政年份:2017
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
CRII: SaTC: Towards Non-Intrusive Detection of Resilient Mobile Malware and Botnet using Application Traffic Measurement
CRII:SaTC:使用应用程序流量测量对弹性移动恶意软件和僵尸网络进行非侵入式检测
- 批准号:
1566388 - 财政年份:2016
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
相似国自然基金
复合低维拓扑材料中等离激元增强光学响应的研究
- 批准号:12374288
- 批准年份:2023
- 资助金额:52 万元
- 项目类别:面上项目
基于管理市场和干预分工视角的消失中等企业:特征事实、内在机制和优化路径
- 批准号:72374217
- 批准年份:2023
- 资助金额:41.00 万元
- 项目类别:面上项目
托卡马克偏滤器中等离子体的多尺度算法与数值模拟研究
- 批准号:12371432
- 批准年份:2023
- 资助金额:43.5 万元
- 项目类别:面上项目
中等质量黑洞附近的暗物质分布及其IMRI系统引力波回波探测
- 批准号:12365008
- 批准年份:2023
- 资助金额:32 万元
- 项目类别:地区科学基金项目
中等垂直风切变下非对称型热带气旋快速增强的物理机制研究
- 批准号:42305004
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
相似海外基金
Collaborative Research: CPS: Medium: Automating Complex Therapeutic Loops with Conflicts in Medical Cyber-Physical Systems
合作研究:CPS:中:自动化医疗网络物理系统中存在冲突的复杂治疗循环
- 批准号:
2322534 - 财政年份:2024
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
Collaborative Research: CPS: Medium: Automating Complex Therapeutic Loops with Conflicts in Medical Cyber-Physical Systems
合作研究:CPS:中:自动化医疗网络物理系统中存在冲突的复杂治疗循环
- 批准号:
2322533 - 财政年份:2024
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
Collaborative Research: CPS: Medium: Physics-Model-Based Neural Networks Redesign for CPS Learning and Control
合作研究:CPS:中:基于物理模型的神经网络重新设计用于 CPS 学习和控制
- 批准号:
2311084 - 财政年份:2023
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
CPS: Medium: Collaborative Research: Provably Safe and Robust Multi-Agent Reinforcement Learning with Applications in Urban Air Mobility
CPS:中:协作研究:可证明安全且鲁棒的多智能体强化学习及其在城市空中交通中的应用
- 批准号:
2312092 - 财政年份:2023
- 资助金额:
$ 70万 - 项目类别:
Standard Grant
Collaborative Research: CPS: Medium: Sensor Attack Detection and Recovery in Cyber-Physical Systems
合作研究:CPS:中:网络物理系统中的传感器攻击检测和恢复
- 批准号:
2333980 - 财政年份:2023
- 资助金额:
$ 70万 - 项目类别:
Standard Grant