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.

创新技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是通过开发和原型化一种新型的基于智能手机的人群传感系统来实现的，该系统可以估计道路的几何特征，如坡度、曲率、高程、交叉坡度和超高程。这种系统可以作为一种具有成本效益和可扩展的替代方案，以替代当前的道路几何形状采集系统，这些系统通常使用使用特殊仪器的车辆/飞机进行地面或空中测量。道路几何信息可以帮助：a)提高人类驾驶和自动驾驶车辆的驾驶安全性和效率，b)改善智能手机/车载定位和导航服务，特别是在全球定位系统（GPS）精度有限的地区，以及c)进行路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形状估计任务的传感平台所带来的独特研究挑战。首先，由于传感器质量较低，智能手机的数据很嘈杂，容易出现漂移和偏差。其次，为了使系统对用户透明，智能手机必须能够被放置在车辆的任意位置和方向。第三，人群传感系统的特点是来自不同来源/车辆的数据的QoI（信息质量）不同，这是由于车辆的不同物理特性、不同智能手机上的传感器质量不同等因素造成的。集成解决方案的成功实施将产生新颖的异构传感器融合算法，通过结合智能手机上多种类型传感器的数据和来自辅助来源（如遥感道路高程）的数据，来处理传感器噪声和任意放置。该项目将开发数据聚合算法，将多个用户的智能手机数据以qos感知的方式组合在一起，以解决单个车辆数据的不可靠性问题。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

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