A Lane-Level Vehicle Positioning Method Based on Fusion of MM and RSSI-DR for VANET Environment
Accurate and reliable vehicle location is a key component of numerous ITS applications. The accuracy of the existing positioning cannot meet the requirements of strong adaptability and high accuracy for the lane-level positioning in the VANET environment. This paper proposes a lane-level vehicle positioning method based on MM (map matching) and RSSI (radio signal strength indication)-DR (dead reckoning). It adopts the hierarchical positioning method: firstly, use MM to realize the rough positioning; secondly; then use RSSI and DR to obtain the relative positioning; and finally, use data fusion to realize the lane-level positioning. The numerical simulation of the whole model is carried out and the optimal weight combination for fusion method is obtained. The average positioning error, lane positioning accuracy, and trajectory difference of different positioning methods are compared to verify the effect of fusion method.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482292
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Supplemental Notes:
- © 2019 American Society of Civil Engineers.
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Corporate Authors:
American Society of Civil Engineers
1801 Alexander Bell Drive
Reston, VA United States 20191-4400 -
Authors:
- Wu, Zhizhou
- Yang, Yue
- Tan, Guishan
- Tian, Ye
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Conference:
- 19th COTA International Conference of Transportation Professionals
- Location: Nanjing , China
- Date: 2019-7-6 to 2019-7-8
- Publication Date: 2019-7
Language
- English
Media Info
- Media Type: Web
- Monograph Title: CICTP 2019: Transportation in China—Connecting the World
Subject/Index Terms
- TRT Terms: Automatic vehicle detection and identification systems; Connected vehicles; Intelligent transportation systems; Vehicle trajectories; Vehicular ad hoc networks
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01712541
- Record Type: Publication
- ISBN: 9780784482292
- Files: TRIS, ASCE
- Created Date: Jul 26 2019 11:52AM