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

• 批准号: 2235231
• 负责人: Qiben Yan
• 金额: $ 70万
• 依托单位: Michigan State University
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235231&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Robust; CPS; Medium; Collaborative; Research; Robust

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。