Collaborative Research: Road Information Discovery through Privacy-Preserved Collaborative Estimation in Connected Vehicles

协作研究：通过联网车辆中保护隐私的协作估计来发现道路信息

• 批准号: 2422579
• 负责人: Minghui Zheng
• 金额: $ 28.85万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2422579&HistoricalAwards=false
• 关键词: Collaborative; Research; Road; Information; Discovery

This project will promote the progress of science and advance the national prosperity and welfare, by investigating novel methodologies leading to efficient road information discovery, as well as safe and efficient transportation systems. Real-time and crowd-sourced road information, such as black ice, pothole, and road roughness, can improve vehicle performance. Existing road information discovery approaches are not always practically viable, due to limitations in road coverage and lack of robustness. This award supports development of a novel collaborative road information crowdsourcing methodology using connected vehicles. The new methodology will enable efficient, robust, and broad-coverage road information discovery by utilizing connected vehicles as mobile sensors while preserving privacy of the participating vehicles. The crowd-sourced information can be incorporated in vehicle controls to improve safety, efficiency, and comfort. In addition, the new up-to-date road condition information will help address the nation’s urgent need to rebuild and modernize road infrastructure, by informing the road maintenance and repair plans. This research involves several disciplines including vehicle dynamics, optimal estimation, iterative learning control, and privacy. The multi-disciplinary approach will help broaden participation of underrepresented groups in research and positively impact engineering education.The privacy-preserved collaborative estimation using connected vehicles is expected to make vehicle-based road information discovery practically and economically viable. This project will support work to overcome several scientific challenges that need to be overcome to realize full application potential of such connected systems. The research team will develop jump-diffusion process-based estimation to enhance road information discovery performance when dealing with abrupt input/disturbance changes in a single vehicle setting. The team will also develop iterative learning-based collaborative estimation across heterogeneous vehicles to enable the exploitation of local estimation from a network of heterogeneous agents to iteratively improve the performance of road information discovery. Finally, the research group will design dynamics-enabled privacy preservation schemes to protect vehicle privacy without affecting computation fidelity or incurring large computation/communication overhead, and evaluate the methodology in the application of collaborative road profile estimation.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.

该项目将通过研究导致高效道路信息发现的新方法以及安全高效的交通系统，促进科学进步，促进国家繁荣和福利。实时和众包的道路信息，如黑冰，坑洞和道路粗糙度，可以提高车辆性能。现有的道路信息发现方法并不总是实际可行的，由于道路覆盖的限制和缺乏鲁棒性。该奖项支持开发一种使用联网车辆的新型协作道路信息众包方法。新方法将通过利用联网车辆作为移动的传感器，同时保护参与车辆的隐私，实现高效、稳健和广泛覆盖的道路信息发现。众包信息可以被整合到车辆控制中，以提高安全性、效率和舒适性。此外，新的最新道路状况信息将有助于满足该国重建和现代化道路基础设施的迫切需要，为道路养护和维修计划提供信息。该研究涉及多个学科，包括车辆动力学、最优估计、迭代学习控制和隐私。多学科方法将有助于扩大代表性不足的群体在研究中的参与，并对工程教育产生积极影响。使用联网车辆的隐私保护协作估计预计将使基于车辆的道路信息发现在实际和经济上可行。该项目将支持克服需要克服的几个科学挑战，以实现这种连接系统的全部应用潜力。研究团队将开发基于跳跃扩散过程的估计，以增强在处理单个车辆设置中的突然输入/干扰变化时的道路信息发现性能。该团队还将开发基于迭代学习的跨异构车辆协作估计，以利用异构代理网络的本地估计，从而迭代地提高道路信息发现的性能。最后，该研究小组将设计动态启用的隐私保护方案，以保护车辆隐私，而不影响计算保真度或产生大量的计算/通信开销，并评估该方法在协作道路轮廓估计中的应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。