Table of Contents
International Journal of Vehicular Technology
Volume 2012, Article ID 465819, 10 pages
http://dx.doi.org/10.1155/2012/465819
Research Article

River Flow Lane Detection and Kalman Filtering-Based B-Spline Lane Tracking

1Electrical and Computer Department, School of Engineering, Curtin University Sarawak, CDT 250, Sarawak, 98009 Miri, Malaysia
2School of Computer Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Selangor, 46150 Petaling Jaya, Malaysia
3Centre for Communications Engineering Research, Edith Cowan University, Joondalup, WA 6027, Australia

Received 27 March 2012; Accepted 26 September 2012

Academic Editor: T. A. Gulliver

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.

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.