Map++：一种自动地图语义识别的众包感知系统

Map++: A Crowd-sensing System for Automatic Map Semantics Identification

        商业的或免费的数字地图服务已经成为我们日常生活中很重要的一部分。这些服务还存在更大的改进空间，比如通过丰富的语义信息来增强它的功能。本文中作者提出了一种Map++系统，能够利用标准的手机传感器以众包感知的方式用不同的道路语义信息丰富数字地图，比如隧道、颠簸路段、桥、人行桥、人行横道和道路容量等。通过分析可知，人们在乘车或行走过程中所携带的手机传感器特征受不同道路特征的影响，因此，可以从中挖掘扩展免费和商业制图服务中的要素。作者设计实现了一套Map++并在大城市中进行了评估。评估结果表明，Map++在行人乘车或行走过程中的检测不同语义精度分别为：错报率（False Positive Rate） 3%、漏报率(False Negative Rate)6%。

     Abstract Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, the authors present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. The analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. They present the design and implementation of Map++ and evaluate it in a large city. The evaluation shows that the approach can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, they show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.

In summary, the contributions are three-fold:

• Present the Map++ architecture to automatically crowdsense and identify map semantics from available sensor readings without inferring any overhead on the user and with minimal energy consumption.

• Provide a framework for extracting the different map features from both pedestrian and in-vehicle traces.

• Implement Map++ on Android devices and evaluate its accuracy and energy-efficiency in a typical city.

The Map++ system architecture

Aly H, Basalamah A, Youssef M. Map++: A crowd-sensing system for automatic map semantics identification[C]// 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), IEEE, 2014:546-554.