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Published VersionCC BY V3.0, Open Access
Journal article
River flow lane detection and Kalman filtering-based B-spline lane tracking
International Journal of Vehicular Technology, Vol.2012, pp.1-11
2012
Abstract
A novel lane detection technique using adaptive line segment and river flow method is proposed in this paper to estimate driving lane edges. A Kalman filtering-based B-spline tracking model is also presented to quickly predict lane boundaries in consecutive frames. Firstly, sky region and road shadows are removed by applying a regional dividing method and road region analysis, respectively. Next, the change of lane orientation is monitored in order to define an adaptive line segment separating the region into near and far fields. In the near field, a 1D Hough transform is used to approximate a pair of lane boundaries. Subsequently, river flow method is applied to obtain lane curvature in the far field. Once the lane boundaries are detected, a B-spline mathematical model is updated using a Kalman filter to continuously track the road edges. Simulation results show that the proposed lane detection and tracking method has good performance with low complexity.
Details
- Title
- River flow lane detection and Kalman filtering-based B-spline lane tracking
- Authors
- King Hann Lim (Author) - Curtin University SarawakKah Phooi Seng (Author) - Sunway UniversityLi-Minn Ang (Author) - Edith Cowan University
- Publication details
- International Journal of Vehicular Technology, Vol.2012, pp.1-11
- Publisher
- Hindawi Publishing Corporation
- Date published
- 2012
- DOI
- 10.1155/2012/465819
- ISSN
- 1687-5702; 1687-5710; 1687-5702
- Copyright note
- Copyright © 2012 King Hann Lim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99513895102621
- Output Type
- Journal article
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