A lane-curve detection based on an LCF

Park, Jong Woung; Lee, Joon Woong; Jhang, Kyung-Young

doi:10.1016/s0167-8655(03)00056-4

Cited by 49 publications

(22 citation statements)

References 10 publications

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“…The localisation of lane markings can also be achieved in several ways, for example, lane-deformable models (Park et al, 2003;Wang et al, 2004) or indeed simple linear models (e.g. Hough transform), which are within the technical specification of ISO 17361:2007 and FMCSA-MCRR-05-005.…”

Section: Lane Departure Warning (Ldw)mentioning

confidence: 99%

Trends Towards Automotive Electronic Vision Systems for Mitigation of Accidents in Safety Critical Situations

Hughes¹,

O’Malley²,

O’Cualain³

et al. 2011

New Trends and Developments in Automotive System Engineering

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Section: Lane Departure Warning (Ldw)mentioning

confidence: 99%

Trends Towards Automotive Electronic Vision Systems for Mitigation of Accidents in Safety Critical Situations

Hughes¹,

O’Malley²,

O’Cualain³

et al. 2011

New Trends and Developments in Automotive System Engineering

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“…Some use lane-mark textures and learning-based method to train a lane-mark classifier [7].Other methods mostly depend on edges [8] [9]. Edge-based methods can work well with either solid or dashed lane-marks, and can be largely invariant to illumination changes.…”

Section: Directional Edge-link Pairsmentioning

confidence: 99%

Lane Detection in Complex Environment Using Grid-Based Morphology and Directional Edge-link Pairs

Lin¹,

Han²,

Hahn³

2010

Journal of Korean Institute of Intelligent Systems

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This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of 320×240.

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“…Deformable road models have been widely used for lane detection [17,16,14,18]. These techniques attempt to determine mathematical models for road boundaries.…”

Section: Related Workmentioning

confidence: 99%