Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102636
Title: LaIF : a lane-level self-positioning scheme for vehicles in GNSS-denied environments
Authors: Rabiee, Ramtin
Zhong, Xionghu
Yan, Yongsheng
Tay, Wee Peng
Keywords: Vehicle Localization
GNSS-denied
Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Rabiee, R., Zhong, X., Yan, Y., & Tay, W. P. (2019). LaIF : a lane-level self-positioning scheme for vehicles in GNSS-denied environments. IEEE Transactions on Intelligent Transportation Systems, 20(8), 2944-2961. doi:10.1109/TITS.2018.2870048
Series/Report no.: IEEE Transactions on Intelligent Transportation Systems
Abstract: 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. We 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 us 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.
URI: https://hdl.handle.net/10356/102636
http://hdl.handle.net/10220/49525
ISSN: 1524-9050
DOI: http://dx.doi.org/10.1109/TITS.2018.2870048
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TITS.2018.2870048.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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