Name
Abstract | ||
|---|---|---|
Lane-level vehicular localization has been regarded as a critical technology component for future vehicle navigation services. Current lane-level vehicular localization systems require dedicated devices, making the systems difficult to popularize. Moreover, most systems depend heavily on continuous accurate GPS data, which may be interfered under specific environments. Efficient lane-level map building is another problem to deal with. In this paper, we propose an integrated system with the capability of map building and lane-level localization using smartphones. This system employs a crowdsourcing-based approach to collect information from multiple sensors (including GPS, orientation sensor and acceleration sensor), such as lane changes and turns. Based on the information, a lane localization schemes is designed using the tool of machine learning. The experimental results show that the proposed system achieves high accuracy of map building and lane-level localization. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE 84TH VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL) | Localzation, Lane Detection, Machine Learning |
Field | DocType | ISSN |
Gps data,Computer science,Crowdsourcing,Support vector machine,Real-time computing,Electronic engineering,Global Positioning System,Acceleration,Multiple sensors,Embedded system | Conference | 2577-2465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyu Zhu | 1 | 42 | 11.41 |
| Xiong Wang | 2 | 70 | 5.15 |
| Zhehui Zhang | 3 | 13 | 3.68 |
| Xiaohua Tian | 4 | 568 | 65.92 |
| Xinbing Wang | 5 | 2642 | 214.43 |