CAREER: Privacy-Aware Collaborative Sensing and Control for Cloud-Enabled Automotive Vehicles

职业：支持云的汽车的隐私感知协作传感和控制

• 批准号: 2045436
• 负责人: Zhaojian Li
• 金额: $ 53.38万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045436&HistoricalAwards=false
• 关键词: CAREER; Privacy; Aware; Collaborative; Sensing

This Faculty Early Career Development Program (CAREER) grant will support research that will contribute novel methodologies related to cloud-enabled automotive vehicles, promoting both the progress of science and advancing transportation safety and efficiency. With the advent of 5G technology, cloud computing is expected to revolutionize automotive applications by providing “big data” and real-time, high-fidelity computing capabilities. Despite its promise, the use of cloud computing in automotive vehicle control and sensing still has limited success due to concerns in communication privacy and real-time constraints inherent to many automotive systems. This award supports fundamental research that addresses the major challenges in cloud-based control, collaborative sensing, decentralized optimization, and privacy preservation. The new designs and methodologies will offer a transformative framework in cloud-facilitated collaborative sensing and control that seamlessly integrate cloud and vehicle resources to enable smarter, safer, and greener next-generation automotive systems. This research is synergistic with key societal goals related to developing efficient, secure, and safe transportation systems. Therefore, results from this research will benefit the U.S. economy and life quality. This research involves several disciplines including control theory, machine learning, vehicle dynamics, and privacy preservation. The multi-disciplinary approach also facilitates the participation of underrepresented groups in research and positively impacts engineering education.The cloud-facilitated collaborative sensing and control is expected to greatly enhance vehicle control performance, and achieve improved safety, energy efficiency, and ride comfort. In pursuit of this goal, four closely integrated research objectives are planned: 1) Develop a novel privacy-preserving, learning-based collaborative sensing framework to enable the exploitation of multiple heterogeneous vehicles to iteratively improve the estimation of important road information (e.g., black ice and pothole) while preserving privacy; 2) Formalize and synthesize privacy-aware cloud-facilitated control to seamlessly integrate cloud and onboard controls for enhanced performance without leaking vehicle privacy; 3) Develop a computationally-efficient, privacy-preserving decentralized control framework by explicitly exploiting the sparse communication/constraint topologies in connected vehicles, and 4) Evaluate and validate the frameworks through extensive simulations and experiments. Collectively, advances from these research endeavors are expected to make cloud-based vehicle controls practically viable, and it will create new accuracy-friendly and computationally-efficient privacy mechanisms for time-sensitive dynamical 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.

这项教师早期职业发展计划（Career）资助将支持研究，这些研究将贡献与云计算汽车相关的新方法，促进科学进步，提高交通安全和效率。随着5G技术的出现，云计算有望通过提供“大数据”和实时、高保真计算能力，彻底改变汽车应用。尽管前景光明，但由于许多汽车系统固有的通信隐私和实时限制，云计算在汽车车辆控制和传感中的应用仍然有限。该奖项支持解决基于云的控制、协同传感、分散优化和隐私保护等主要挑战的基础研究。新的设计和方法将为云协同传感和控制提供一个变革性框架，无缝集成云和车辆资源，以实现更智能、更安全、更环保的下一代汽车系统。这项研究与发展高效、安全、安全的交通系统相关的关键社会目标是协同的。因此，这项研究的结果将有利于美国的经济和生活质量。这项研究涉及多个学科，包括控制理论、机器学习、车辆动力学和隐私保护。多学科方法也促进了代表性不足的群体参与研究，并对工程教育产生积极影响。云辅助的协同传感和控制有望大大提高车辆控制性能，并实现更高的安全性、能源效率和乘坐舒适性。为了实现这一目标，计划开展四个紧密结合的研究目标：1)开发一种新的基于学习的隐私保护协同传感框架，使多异构车辆能够在保护隐私的同时迭代改进对重要道路信息（如黑冰和坑洼）的估计；2)形式化和综合隐私感知云辅助控制，无缝集成云和车载控制，在不泄露车辆隐私的情况下提高性能；3)通过明确利用互联车辆中的稀疏通信/约束拓扑，开发计算效率高、保护隐私的分散控制框架；4)通过广泛的模拟和实验评估和验证框架。总的来说，这些研究工作的进展有望使基于云的车辆控制切实可行，并将为时间敏感的动态系统创造新的精度友好和计算效率高的隐私机制。该奖项反映了美国国家科学基金会的法定使命，并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。

项目成果

Simultaneous Suspension Control and Energy Harvesting Through Novel Design and Control of a New Nonlinear Energy Harvesting Shock Absorber

• DOI: 10.1109/tvt.2022.3159734
• 发表时间: 2021-06
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Mohammad R. Hajidavalloo;Joel A. Cosner;Zhaojian Li;Wei-Che Tai;Ziyou Song

Privacy-Preserving Collaborative Estimation for Networked Vehicles With Application to Collaborative Road Profile Estimation

• DOI: 10.1109/tits.2022.3154650
• 发表时间: 2022-10
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Huan Gao;Zhaojian Li;Yongqiang Wang

Cloud-Assisted Collaborative Road Information Discovery With Gaussian Process: Application to Road Profile Estimation

• DOI: 10.1109/tits.2022.3194093
• 发表时间: 2021-10
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Mohammad R. Hajidavalloo;Zhaojian Li;Xin Xia;Ali Louati;Minghui Zheng;Weichao Zhuang

Privacy-Preserving Dynamic Average Consensus via State Decomposition: Case Study on Multi-Robot Formation Control

• DOI: 10.1016/j.automatica.2022.110182
• 发表时间: 2020-08
• 期刊: ArXiv
• 作者: Kaixiang Zhang;Zhaojian Li;Yongqiang Wang;Ali Louati;Jian Chen

Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks

• DOI: 10.1109/tac.2022.3219293
• 发表时间: 2021-06
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Nan Li;Kaixiang Zhang;Zhaojian Li;Vaibhav Srivastava;Xiang Yin