LaIF: A Lane-Level Self-Positioning Scheme for Vehicles in GNSS-Denied Environments
Vehicle self-positioning is of significant importance for intelligent transportation applications. However, accurate positioning (e.g., with lane-level accuracy) is very difficult to obtain due to the lack of measurements with high confidence, especially in an environment without full access to a global navigation satellite system (GNSS). In this paper, a novel information fusion algorithm based on a particle filter is proposed to achieve lane-level tracking accuracy under a GNSS-denied environment. The authors consider the use of both coarse-scale and fine-scale signal measurements for positioning. Time-of-arrival measurements using the radio frequency signals from known transmitters or roadside units, and acceleration or gyroscope measurements from an inertial measurement unit (IMU) allow the authors to form a coarse estimate of the vehicle position using an extended Kalman filter. Subsequently, fine-scale measurements, including lane-change detection, radar ranging from the known obstacles (e.g., guardrails), and information from a high-resolution digital map, are incorporated to refine the position estimates. A probabilistic model is introduced to characterize the lane changing behaviors, and a multi-hypothesis model is formulated for the radar range measurements to robustly weigh the particles and refine the tracking results. Moreover, a decision fusion mechanism is proposed to achieve a higher reliability in the lane-change detection as compared to each individual detector using IMU and visual (if available) information. The posterior Cramér–Rao lower bound is also derived to provide a theoretical performance guideline. The performance of the proposed tracking framework is verified by simulations and real measured IMU data in a four-lane highway.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2019, IEEE.
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Authors:
- Rabiee, Ramin
- Zhong, Xionghu
- Yan, Yongsherig
- Tay, Wee Peng
- Publication Date: 2019-8
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 2944-2961
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 20
- Issue Number: 8
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Algorithms; Automatic vehicle location; Data fusion; Inertial navigation systems; Kalman filtering; Satellite navigation systems; Traffic lanes
- Uncontrolled Terms: Localization
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01715800
- Record Type: Publication
- Files: TLIB, TRIS
- Created Date: Sep 3 2019 9:15AM