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

King Hann Lim, Kah Phooi Seng, Li-Minn Ang

Research output: Contribution to journalArticle

20 Citations (Scopus)

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.
Original languageEnglish
Article number465819
Pages (from-to)1-10
Number of pages10
JournalInternational Journal of Vehicular Technology
Volume2012
DOIs
Publication statusPublished - 2012

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Splines
Rivers
Hough transforms
Kalman filters
Mathematical models

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Lim, King Hann ; Seng, Kah Phooi ; Ang, Li-Minn. / River Flow Lane Detection and Kalman Filtering-Based B-Spline Lane Tracking. In: International Journal of Vehicular Technology. 2012 ; Vol. 2012. pp. 1-10.
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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.",
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River Flow Lane Detection and Kalman Filtering-Based B-Spline Lane Tracking. / Lim, King Hann; Seng, Kah Phooi; Ang, Li-Minn.

In: International Journal of Vehicular Technology, Vol. 2012, 465819, 2012, p. 1-10.

Research output: Contribution to journalArticle

TY - JOUR

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AU - Lim, King Hann

AU - Seng, Kah Phooi

AU - Ang, Li-Minn

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AB - 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.

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KW - Lane lines (Roads)

KW - Signal detection

KW - Splines

KW - Kalman filtering

KW - Prediction models

KW - Approximation theory

KW - Simulation methods & models

KW - Performance evaluation

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