PFI-TT: Crowdsourced Road Geometry Estimation using Smartphones

该创新技术转化合作伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是通过开发基于智能手机的新型人群感应系统并进行原型设计来实现的，该系统可以估计道路几何特征，如坡度、曲率、高程、横坡和超高。这样的系统可以潜在地充当当前道路几何形状获取系统的成本有效且可扩展的替代方案，当前道路几何形状获取系统通常采用使用专门仪器化的车辆/飞行器的地面或航空勘测。关于道路几何形状的信息可以帮助：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）进行风险评估和路段工程设计。该项目旨在解决使用智能手机作为道路几何估计任务的传感平台所带来的独特研究挑战。首先，由于低质量的传感器，来自智能手机的数据是嘈杂的，容易漂移和偏见。其次，为了使系统对用户透明，智能手机必须能够放置在车辆中的任意位置和方向。第三，人群感测系统的特征在于变化的QoI由于车辆的物理特性不同、不同智能手机上的传感器质量不同等因素，来自不同来源/车辆的数据的质量（信息质量）不同。异构传感器融合算法，通过组合来自智能手机上的多种类型传感器的数据和来自辅助传感器的数据来处理传感器噪声和任意放置。例如遥感道路高程。该项目将导致数据聚合算法的开发，该算法将联合收割机多个用户的智能手机数据以一种QOI感知的方式结合起来，以解决单个车辆数据的不可靠性。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。

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.