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.

该项目将通过调查导致有效的道路信息发现以及安全有效的运输系统的新方法来促进科学的进步，并促进国家繁荣和福利的发展。实时和众包的道路信息，例如黑冰，坑洼和道路粗糙度，可以改善车辆性能。由于道路覆盖范围的局限性和缺乏鲁棒性，现有的道路信息发现方法并不总是可行的。该奖项支持使用互联车辆的新型协作道路信息众包方法的开发。新方法将通过使用连接的车辆作为移动传感器，同时保留参与车辆的私密性，从而实现高效，健壮和宽覆盖的道路信息发现。人群信息可以将其纳入车辆控制中，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通过通知道路维护和维修计划来帮助该国迫切需要重建和现代化道路基础设施。这项研究涉及多个学科，包括车辆动态，最佳估计，迭代学习控制和隐私。多学科的方法将有助于扩大代表性不足的群体参与研究和对工程教育的积极影响。预计使用连接的车辆的隐私权协作估算有望使基于车辆的道路信息实际上可行，并在经济上可行。该项目将支持克服一些科学挑战的工作，这些挑战需要克服，以实现此类连接系统的全面应用潜力。研究团队将在处理单个车辆设置中的突然输入/干扰变化时开发基于跳跃的过程的估计，以增强道路信息发现性能。该团队还将开发基于迭代学习的协作估计，跨越异构车辆，以使当地估计从异质代理网络中剥削到迭代地改善道路信息发现的性能。最后，研究小组将设计具有动态的隐私保护计划，以保护车辆隐私而不影响计算忠诚度或产生大型计算/通信开销，并评估在应用协作道路概况估算中的方法中。这项奖项反映了NSF的立法任务，并通过使用基础的智力评估来诚实地进行评估，以诚实地评估基础的智力和广泛的影响。