Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision

Song, Wenjie; Yang, Yi; Fu, Mengyin; Li, Yujun; Wang, Meiling

doi:10.1109/jsen.2018.2832291

Cited by 105 publications

(53 citation statements)

References 34 publications

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“…Because the one-dimensional data represent the horizontal ordinate values, and the lane line in the image is the image pixel matrix, the one-dimensional data is non-negative. Before the gray forecast model is set, x (0) should be calculated once: X (1) = X (1)…”

Section: Feature Point Tracking Of the Lane Linementioning

confidence: 99%

“…In recent years, advanced driver assistance systems (ADAS) and autonomous driving are becoming more and more important to reducing traffic accidents. As a key technology for intelligent vehicles, lane detection has attracted widespread attention from plenty of institutes and automobile technology companies [1]. Among the research, vision-based lane detection has always been a hot topic in the field of lane line detection.…”

Section: Introductionmentioning

confidence: 99%

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Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

Xiong

Sun

et al. 2020

Applied Sciences

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A novel lane detection approach, based on the dynamic region of interest (DROI) selection in the horizontal and vertical safety vision, is proposed to improve the accuracy of lane detection in this paper. The curvature of each point on the edge of the road and the maximum safe distance, which are solved by the lane line equation and vehicle speed data of the previous frame, are used to accurately select the DROI at the current moment. Next, the global search of DROI is applied to identify the lane line feature points. Subsequently, the discontinuous points are processed by interpolation. To fulfill fast and accurate matching of lane feature points and mathematical equations, the lane line is fitted in the polar coordinate equation. The proposed approach was verified by the Caltech database, under the premise of ensuring real-time performance. The accuracy rate was 99.21% which is superior to other mainstream methods described in the literature. Furthermore, to test the robustness of the proposed method, it was tested in 5683 frames of complicated real road pictures, and the positive detection rate was 99.07%.

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Section: Feature Point Tracking Of the Lane Linementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

Xiong

Sun

et al. 2020

Applied Sciences

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show abstract

“…The system detects the lane as well as track the lane also. Song et al [12] presented a system that can detect the lane as well as classify them using the concept of stereo vision for the essence of Advanced Driver Assistance Systems (ADAS). They proposed a model to detect the lane using the idea of Region of Interest (ROI), and for the classification task, they used Convolutional Neural Network (CNN) structure that is trained with the KITTI dataset to classify the right or left lane.…”

Section: Related Workmentioning

confidence: 99%

A Computer Vision based Lane Detection Approach

Haque¹,

Islam²,

Alam³

et al. 2019

IJIGSP

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Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.

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“…However, images as well as their interpretations (i.e. segmented pixels) in this perspective are often transformed into a local and/or global coordinate system (or view) to be utilized effectively within tasks such as lane detection [3], [4], road marking detection [5], road topology detection [6], [7], object detection/tracking [8]- [10], as well as path planning and intersection prediction [11], [12]. This transformation is commonly referred to as Inverse Perspective Mapping (IPM) [13].…”

Section: Introductionmentioning

confidence: 99%

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Bruls

Porav

Kunze

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird'seye-view images contain sharper features (e.g. road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford Robot-Car Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.

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Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision

Cited by 105 publications

References 34 publications

Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

A Computer Vision based Lane Detection Approach

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

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