An Improved Linear-Parabolic Model for Lane Following and Curve Detection

Jung, Cláudio Rosito; Kelber, C.R.

doi:10.1109/sibgrapi.2005.10

Cited by 45 publications

(16 citation statements)

References 17 publications

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“…Canny edge filter was applied on the divided regions to acquire the possible lane edges. lung and Kelber [3] presented a linear-parabolic lane model by manually cropping the effective lane region for lane detection and tracking. Sobel edge detector was applied to obtain the edges along the road.…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Improvement of lane marks extraction technique under different road conditions

Lim

Seng

Ang

2010

2010 3rd International Conference on Computer Science and Information Technology

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Ahstract-The application of edge detection to obtain salient lane boundaries on road image is popular in the computer vision area. However, edge detectors may be easily distracted by manifold noises that emerged on the road surface such as shadow cast, vehicles or other obstacles. Additionally, sky region can adversely affect the performance of lane detection method due to the presence of horizontal edges in the sky region. In this paper, lane preprocessing approach is proposed to effectively extract lane marks from the traffic scene. Horizon localization is improved to automatically segment the sky and road region through regional minimum search. It is followed by the lane region analysis to adaptively remove the road pixels and obstacles. Evaluations on the proposed lane preprocessing method are performed under different time of the day/night and various road sceneries such as city, highway, rural, suburban etc. Some results and findings are shown in the paper with regards on the observation of the road intensity change respective to the variation of the road conditions.

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Section: Introductionmentioning

confidence: 99%

Notice of Retraction: Improvement of lane marks extraction technique under different road conditions

Lim

Seng

Ang

2010

2010 3rd International Conference on Computer Science and Information Technology

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“…Back-projection techniques assume constant road width [2], and thus the estimated road shape can have arbitrary errors. Road shapes have been modeled as straight lines [9][18] [19], parabolae [6] [17], circles [4], cubic splines [16] [20] and Taylor series approximation of clothoids [3] [11], etc.…”

Section: A Backgroundmentioning

confidence: 99%

“…Once a safe speed υ u for the vehicle is calculated from l u for a given curvature using Eq (13), (14) and (15); the corresponding steering rate ω can be calculated recursively from current frame F t to F t+1 using Eq (17). Thus, the relationship between image plane and ground plane velocity is derived in terms of image plane parameters, the vehicle control parameters υ u , ω, θ can be computed directly from the image plane, and a transformation to the ground plane is not necessary.…”

Section: B Predictive Control In Image Co-ordinatesmentioning

confidence: 99%

Concurrent visual multiple lane detection for autonomous vehicles

Gupta

Snyder

Pitts

2010

2010 IEEE International Conference on Robotics and Automation

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This paper proposes a monocular vision solution to simultaneous detection of multiple lanes in navigable regions / urban roads using accumulator voting. Unlike other approaches in literature, this paper first examines the extent of lane parameters required for continuous control of any vehicle manually or autonomously. The accumulator-based algorithm is designed using this fundamental control knowledge to vote for the required lane parameters (position of lanes and steering angle required) in the image plane. The novel accumulator voting scheme is called "Parametric Transform for Multi-lane Detection." This paper not only adapts predictive control in the image plane, but also detects multiple lanes in the scene concurrently in the form of multiple peaks in the accumulator. This method is robust to shadows and invariant to color, texture, and width of the road. Finally, the method is designed for dashed/continuous lines.

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“…Over the past decades, many road detection approaches [16][17][18] have been developed, but the importance of this task leads researchers to work more to solve this problem taking advantage of camera and computer vision evolution. [17] In the present paper, the basic goal is concerned with road boundary detection and extraction of the road form.…”

Section: Introductionmentioning

confidence: 99%

Road intersection detection and classification using hierarchical SVM classifier

2014

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In this paper, a hierarchical multi-classification approach using support vector machines (SVM) has been proposed for road intersection detection and classification. Our method has two main steps. The first involves the road detection. For this purpose, an edge-based approach has been developed using the bird's eye view image which is mapped from the perspective view of the road scene. Then, the concept of vertical spoke has been introduced for road boundary form extraction. The second step deals with the problem of road intersection detection and classification. It consists on building a hierarchical SVM classifier of the extracted road forms using the unbalanced decision tree architecture. Many measures are incorporated for good evaluation of the proposed solution. The obtained results are compared to those of Choi et al. (2007).

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An Improved Linear-Parabolic Model for Lane Following and Curve Detection

Abstract: In

Cited by 45 publications

References 17 publications

Notice of Retraction: Improvement of lane marks extraction technique under different road conditions

Notice of Retraction: Improvement of lane marks extraction technique under different road conditions

Concurrent visual multiple lane detection for autonomous vehicles

Road intersection detection and classification using hierarchical SVM classifier

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