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

该项目将通过研究新的方法来促进科学的进步，促进国家的繁荣和福祉，从而有效地发现道路信息，以及安全高效的交通系统。实时和众包的道路信息，如黑冰、坑洼和道路粗糙度，可以提高车辆性能。由于道路覆盖范围的限制和鲁棒性的缺乏，现有的道路信息发现方法并不总是实际可行的。该奖项支持开发一种使用互联车辆的新型协同道路信息众包方法。新方法将通过利用联网车辆作为移动传感器，实现高效、稳健和广泛覆盖的道路信息发现，同时保护参与车辆的隐私。众包信息可以整合到车辆控制中，以提高安全性、效率和舒适性。此外，最新的道路状况信息将有助于解决国家重建和现代化道路基础设施的迫切需要，为道路维护和维修计划提供信息。这项研究涉及多个学科，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。使用互联车辆的隐私保护协同估计有望使基于车辆的道路信息发现在实践和经济上可行。该项目将支持克服几个科学挑战的工作，这些挑战需要克服，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，以提高在处理单个车辆设置中突然输入/干扰变化时的道路信息发现性能。该团队还将开发跨异构车辆的基于学习的迭代协作估计，以利用来自异构代理网络的本地估计，以迭代地提高道路信息发现的性能。最后，研究小组将设计动态隐私保护方案，在不影响计算保真度或产生大量计算/通信开销的情况下保护车辆隐私，并评估该方法在协同道路轮廓估计中的应用。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。

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.