CRII: CPS: Cooperative Neuro-Inspired Actor Critic Model for Anomaly Detection in Connected Vehicles

CRII：CPS：用于联网车辆异常检测的协作神经启发演员评论家模型

• 批准号: 2153510
• 负责人: Heena Rathore
• 金额: $ 17.5万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153510&HistoricalAwards=false
• 关键词: CRII; CPS; Cooperative; Neuro; Inspired

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Connected vehicles are an integral part of the future of intelligent transportation systems. They use wireless and sensing technologies to enable communication and cooperation between vehicles and infrastructure. Nonetheless, sensor reliability and data integrity play a crucial role in these vehicles. As vehicles and infrastructures grow increasingly networked and automated, there is a pressing need to identify sensor-related anomalies and mitigate potential safety hazards they might pose. The overarching goal of this project is to protect the connected vehicular network against anomalous sensor readings from any cause to ensure the safety of vehicles and passengers. The research aims to (1) provide new capabilities to broadly address safety concerns in connected vehicles to meet emerging future needs of intelligent transportation systems, and (2) enable a diverse and inclusive community of scientists and engineers to work in multidisciplinary areas such as cognitive machine learning and cybersecurity.With the ever increasing complexity of connected vehicles operating in a more complicated cyber-physical social environment, conventional anomaly detection methods will likely not be able to keep pace with the demands of these challenges and function safely in a tomorrow's smart and connected communities. This project will explore (1) novel algorithmic methods that will enable the vehicles to quickly classify different types of sensor failures, learn new emerging anomalous patterns of sensor activity, and assess their risks relative to vehicle safety, and (2) designs for efficient scalable safe multi-agent models to build reputational trust among the connected vehicles in order to facilitate V2V information sharing, learning, and cooperative decision-making, and (3) new consensus-based protocols for connected vehicles that provide for resilience and adaptivity in the presence of disruptions, interruptions, and changes to vehicle participation. Initial test and evaluations are conducted by computer simulations with publicly-available data sets on connected vehicles and autonomous systems.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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。联网车辆是智能交通系统未来不可或缺的一部分。它们使用无线和传感技术来实现车辆和基础设施之间的通信和合作。尽管如此，传感器的可靠性和数据完整性在这些车辆中起着至关重要的作用。随着车辆和基础设施日益网络化和自动化，迫切需要识别与传感器相关的异常，并减轻它们可能构成的潜在安全危险。该项目的总体目标是保护联网的车辆网络免受任何原因的异常传感器读数的影响，以确保车辆和乘客的安全。这项研究旨在(1)提供新的能力，广泛解决联网车辆的安全问题，以满足智能交通系统未来不断增长的需求，以及(2)使科学家和工程师组成的多样化和包容性社区能够在认知机器学习和网络安全等多学科领域工作。随着联网车辆在更复杂的网络物理社会环境中运行的日益复杂，传统的异常检测方法可能无法跟上这些挑战的需求，并在明天的智能互联社区中安全运行。该项目将探索(1)新的算法方法，使车辆能够快速对不同类型的传感器故障进行分类，了解新出现的传感器活动异常模式，并评估其与车辆安全相关的风险，(2)设计高效、可扩展的安全多代理模型，以在互联车辆之间建立声誉信任，以促进V2V信息共享、学习和合作决策，以及(3)基于共识的新协议，用于互联车辆，在出现中断、中断和车辆参与变化时提供弹性和适应性。最初的测试和评估是通过计算机模拟进行的，使用的是关于联网车辆和自动驾驶系统的公开数据集。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Intelligent Decision Making in Autonomous Vehicles using Cognition Aided Reinforcement Learning

• DOI: 10.1109/wcnc51071.2022.9771728
• 发表时间: 2022-04
• 期刊: 2022 IEEE Wireless Communications and Networking Conference (WCNC)
• 作者: Heena Rathore;V. Bhadauria