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: 2235232
• 负责人: Wenjing Lou
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and 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=2235232&HistoricalAwards=false
• 关键词: 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.

自动驾驶即将彻底改变交通系统，并显着改善人们的福祉。自动驾驶汽车依赖于多个传感器和人工智能算法来促进感知和感知，以导航世界。由于汽车行业主要关注在主要是良性环境中提高自主水平和增强感知性能，因此感知技术对物理攻击的安全性和安全性尚未得到彻底研究。具体来说，制造物理世界感知错觉的对手可能会对自动驾驶汽车的感知和学习系统构成重大威胁，可能会破坏对这些系统的信任。该研究项目旨在加深我们对物理攻击下的安全和安全风险的理解。该项目致力于增强自动驾驶中的感知和学习弹性，以对抗恶意感知错觉攻击。该项目的成功将大大提高自动驾驶在面对新出现的物理世界威胁时的安全性，为自动驾驶汽车在下一代交通系统中的安全部署铺平道路。该项目的目标是研究先进的传感和学习技术，以提高自动驾驶在复杂和恶劣环境中的精度和鲁棒性。该团队的方法包括：（i）一个全面的框架，以评估自动驾驶系统的软件/硬件组件中的关键漏洞，并设计有效的攻击向量，以产生虚假和欺骗性的感知;（ii）一种实时超分辨率雷达传感技术和一种数据融合方法，该方法在中期和后期整合了各种传感器类型的特征，以有效地加强每种传感模式对错觉的鲁棒性;以及（iii）系统框架，以增强算法的通用性，并使用多视图表示学习来实现对多模态攻击的鲁棒感知。所提出的解决方案将通过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、更强大的自动驾驶系统，能够抵御现实场景中的感知错觉攻击。该项目还将为不同级别和年龄组的代表性不足的学生提供研究培训机会。该奖项反映了NSF的法定使命，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。