Lane Detection Based on Road Module and Extended Kalman Filter

Xiao, Jinsheng; Luo, Li; Yuan, Ye; Zou, Wentao; Klette, Reinhard

doi:10.1007/978-3-319-75786-5_31

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…Ridge feature is detected by scan-line method as in many other studies. 7,8 Each row in the image is a scan-line, for each pixel ( x, y ), as in Xiao et al, 7 the gradient G ( x, y ) is calculated by the following formula…”

Section: Lane Markers Extractionmentioning

confidence: 99%

“…As an extension of edge detection, ridge detection finds the low–high–low property of lane markers, which considers width between possible left and right edges; therefore, it usually gets a better performance in the feature extraction step. 6–8…”

Section: Introductionmentioning

confidence: 99%

“…Kalman filter is the most widely used lane tracking method. 7,14,15 With consecutive input, lanes are stable features which can be confirmed by tracking, while random noises will be removed.…”

Section: Introductionmentioning

confidence: 99%

“…As an extension of edge detection, ridge detection finds the low-high-low property of lane markers, which considers width between possible left and right edges; therefore, it usually gets a better performance in the feature extraction step. [6][7][8] Color is another feature to distinguish lane markers from the image. [9][10][11][12] The lane markers are in white and yellow color, brighter than the gray road, making it possible to detect lanes with color characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Real-time lane detection and tracking for autonomous vehicle applications

Jiao

Yang

Jiang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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This article proposes an improved lane detection and tracking method for autonomous vehicle applications. In real applications, when the pose and position of the camera are changed, parameters and thresholds in the algorithms need fine adjustment. In order to improve adaptability to different perspective conditions, a width-adaptive lane detection method is proposed. As a useful reference to reduce noises, vanishing point is widely applied in lane detection studies. However, vanishing point detection based on original image consumes many calculation resources. In order to improve the calculation efficiency for real-time applications, we proposed a simplified vanishing point detection method. In the feature extraction step, a scan-line method is applied to detect lane ridge features, the width threshold of which is set automatically based on lane tracking. With clustering, validating, and model fitting, lane candidates are obtained from the basic ridge features. A lane-voted vanishing point is obtained by the simplified grid-based method, then applied to filter out noises. Finally, a multi-lane tracking Kalman filter is applied, the confirmed lines of which also provide adaptive width threshold for ridge feature extraction. Real-road experimental results based on our intelligent vehicle testbed proved the validity and robustness of the proposed method.

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Section: Lane Markers Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-time lane detection and tracking for autonomous vehicle applications

Jiao

Yang

Jiang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…For example, Loose et al [37] used a Bézier curve to fit the road boundary. Lane detection is also important in the feature space, and numerous studies have been conducted on rule-based lane detection [38][39][40]. In recent years, some researchers used deep learning in lane detection [41,42].…”

Section: Construction Of the Feature Spacementioning

confidence: 99%

Driving Space for Autonomous Vehicles

et al. 2019

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Driving space for autonomous vehicles (AVs) is a simplified representation of real driving environments that helps facilitate driving decision processes. Existing literatures present numerous methods for constructing driving spaces, which is a fundamental step in AV development. This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions: how to reconstruct the driving environment, and how to make driving decisions within the constructed driving space. Furthermore, the advantages and disadvantages of different types of driving space are analyzed. The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.

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Lane Detection Based on Histogram of Oriented Vanishing Points

Shizeng

Guo

et al. 2020

Communications in Computer and Information Science

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Lane Detection Based on Road Module and Extended Kalman Filter

Cited by 9 publications

References 17 publications

Real-time lane detection and tracking for autonomous vehicle applications

Real-time lane detection and tracking for autonomous vehicle applications

Driving Space for Autonomous Vehicles

Lane Detection Based on Histogram of Oriented Vanishing Points

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