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，自动驾驶汽车依靠多个传感器和人工智能算法来促进感知和感知，以实现汽车行业的自动驾驶。水平和增强主要良性环境中的感知性能，针对物理攻击的感知技术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对自动驾驶车辆的感知和学习系统构成重大威胁。 , 潜在地该研究项目旨在加深我们对物理攻击下的安全风险的理解。该项目致力于增强自动驾驶针对恶意感知错觉攻击的感知和学习能力。面对新兴的物理世界威胁，自动驾驶的安全性和安全性，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，以提高精度以及自动驾驶在复杂和敌对环境中的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并设计有效的攻击向量以产生错误和欺骗性感知的综合框架；(ii) 实时超分辨率雷达传感。技术和数据融合方法，在中后期集成各种传感器类型的特征，以有效增强每种传感模式对错觉的鲁棒性；以及（iii）一个系统框架，以增强算法的通用性并实现针对错觉的鲁棒感知；使用多视图表示学习的多模式攻击。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，由此产生的新技术将作为开源共享，以更广泛地传播和推进通过该项目开发的知识。 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。