PFI-TT: Crowdsourced Road Geometry Estimation using Smartphones

PFI-TT：使用智能手机进行众包道路几何估计

• 批准号: 2044670
• 负责人: Chunming Qiao
• 金额: $ 25万
• 依托单位: SUNY at Buffalo
• 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: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044670&HistoricalAwards=false
• 关键词: PFI; TT; Crowdsourced; Road; Geometry

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is enabled by the development and prototyping of a novel smartphone-based, crowd-sensing system that can estimate road geometry features such as grade, curvature, elevation, cross slope, and superelevation. Such a system can potentially act as a cost-effective and scalable alternative to current road geometry acquisition systems which typically employ ground or aerial surveys using specially-instrumented vehicles/aircrafts. Information on road geometry can help in: a) improving driving-safety and efficiency for both human-driven and autonomous vehicles, b) improving smartphone/in-car localization and navigation services, especially in areas with limited global positioning system (GPS) accuracy, and c) conducting risk assessment and engineering design of road segments. The project seeks to address the unique research challenges introduced using smartphones as a sensing platform for the task of road geometry estimation. First, due to low quality sensors, data from smartphones is noisy and prone to drifts and biases. Second, to make the system transparent to users, the Smartphone must be able to be placed in any arbitrary position and orientation in the vehicle. Third, the crowd-sensing system is characterized by varying QoI (Quality of Information) of data from different sources/vehicles due to factors such as varying physical properties of vehicles, varying quality of sensors on different smartphones, etc. Successful implementation of the integrated solution will result in novel, heterogeneous sensor fusion algorithms to handle sensor noise and arbitrary placement by combining data from multiple types of sensors on the smartphone and data from auxiliary sources such as remotely sensed road elevation. The project will result in the development of data aggregation algorithms that combine multiple users’ smartphone data in a QoI-aware manner to address the unreliability of individual vehicle data.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.

这种伙伴关系对创新 - 技术翻译（PFI-TT）项目的更广泛的影响/商业潜力是由一种基于智能手机的新型，人群感应系统的开发和原型来启用的，该系统可以估算诸如等级，曲率，曲率，抬高，交叉坡度和超级范围之类的道路几何特征。这样的系统可以可能充当当前道路几何采集系统的经济高效且可扩展的替代品，该系统通常使用特殊的车辆/飞机采用地面或空中调查。关于道路几何形状的信息可以帮助：a）提高人类驱动和自动驾驶汽车的驾驶安全和效率，b）改善智能手机/车内定位和导航服务，尤其是在全球定位系统（GPS）精度有限的领域，以及c）进行道路分段的风险评估和工程设计。该项目旨在解决使用智能手机作为道路几何估计任务的灵敏度平台引入的独特研究挑战。首先，由于质量低的传感器，智能手机的数据是噪音，容易出现训练和偏见。其次，为了使系统透明，智能手机必须能够将车辆的任意位置和方向放置。第三，人群感应系统的特征是由于因素（例如不同的智能手机的物理性能，不同智能手机上的传感器的变化质量），来自不同来源/车辆的数据的QOI（信息质量）的特征。综合解决方案的成功实现将导致新颖的，新颖的传感器融合量的智能数据和仲裁数据，从而使传感器噪声的多元化数据和数据均可通过，从而使传感器融合的智能数据和数据均可成功实现。辅助来源，例如远程感知的道路高程。该项目将导致数据聚合算法的发展，这些算法以Qoi的方式将多个用户的智能手机数据结合在一起，以解决单个车辆数据的不可靠性。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来获得的支持。

项目成果

Estimation of Road Transverse Slope Using Crowd-Sourced Data from Smartphones

使用智能手机的众包数据估算道路横向坡度

• DOI: 10.1145/3397536.3422239
• 发表时间: 2020
• 期刊: SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems
• 作者: Gupta, Abhishek;Khare, Abhinav;Jin, Haiming;Sadek, Adel;Su, Lu;Qiao, Chunming
• 通讯作者: Qiao, Chunming