Use of Intelligent Navigation and Crowd Collaboration for Automated Collection of Data on Transport Infrastructure

IF 0.8 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
T. Tvrzský
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引用次数: 0

Abstract

The article briefly presents the main results of an applied research project to the professional public. The project output is a solution that enables the recognition of selected types of traffic signs using artificial intelligence for image recognition. This computationally intensive process is implemented in mobile phones. In order to achieve the involvement of the general public in the collection of data on transport infrastructure, the entire solution is part of navigation for mobile phones and supported by two functions that motivate users to collect data, i.e., scan the area in front of the vehicle with the phone's camera. The first function is the projection of the route into the real environment (the so-called augmented reality mode), and the second function is the possibility of video recording the drive. The video recording is cryptographically signed to ensure authenticity in administrative or judicial proceedings, e.g., when proving the course and circumstances of a traffic accident. The collection of data on transport infrastructure is completely anonymous in compliance with applicable laws. The data about recognized traffic signs will not only serve the navigation provider to improve the user experience but the processed data will also be exported to community-created world maps (project OpenStreetMap).
使用智能导航和人群协作自动收集交通基础设施数据
本文向专业公众简要介绍了一个应用研究项目的主要成果。该项目的成果是一种解决方案,能够使用人工智能进行图像识别,识别选定类型的交通标志。这种计算密集型过程在移动电话中实现。为了让公众参与交通基础设施数据的收集,整个解决方案是手机导航的一部分,并由两个功能支持,这两个功能激励用户收集数据,即用手机摄像头扫描车辆前方区域。第一个功能是将路线投影到真实环境中(所谓的增强现实模式),第二个功能是视频记录驾驶过程的可能性。视频记录经过加密签名,以确保在行政或司法程序中的真实性,例如在证明交通事故的过程和情况时。根据适用法律,运输基础设施数据的收集是完全匿名的。有关已识别交通标志的数据不仅将为导航提供商提供服务,以改善用户体验,而且处理后的数据还将导出到社区创建的世界地图(项目OpenStreetMap)中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Acta Informatica Pragensia
Acta Informatica Pragensia Social Sciences-Library and Information Sciences
CiteScore
1.70
自引率
0.00%
发文量
26
审稿时长
12 weeks
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