A novel system for robust lane detection and tracking

Wang, Yifei; Dahnoun, Naim; Achim, Alin

doi:10.1016/j.sigpro.2011.07.019

Cited by 109 publications

(78 citation statements)

References 20 publications

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“…By zooming into the vanishing point of the lanes, the lane markings will only move on the same straight lines they are on [33]. Based on this fact, a lane feature extraction approach was presented [33], [34].…”

Section: B Feature Extraction 1) Lane Featurementioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: B Feature Extraction 1) Lane Featurementioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

125

View full text Add to dashboard Cite

show abstract

“…(5) (6) To reduce the computation of the vector G, we approximate the square root operations with absolute value. Hence, the vector G is reassigned by Eq.…”

Section: (4)mentioning

confidence: 99%

“…The algorithm of curb localization depended on both photometry and range information. In [6], Wang et al present a lane edge feature extraction algorithm. The method is based on the observation that by zooming towards the vanishing point and comparing the zoomed image with the original image allows authors to remove most of the unwanted features from the lane feature map.…”

Section: Introductionmentioning

confidence: 99%

Lane Detection Algorithm for Night-time Digital Image Based on Distribution Feature of Boundary Pixels

Feng¹,

Zhang²,

Zhong³

et al. 2013

Journal of the Optical Society of Korea

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This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions-horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.

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“…All of them rely on standard cameras and are characterized by a significant computational burden. Some of the solutions proposed in literature for path detection and tracking combine specifically conceived image features [30], [31], known path models [32], statistical methods [33] or a combination of particle filtering and artificial intelligence [34]. Another important field of research, which is somehow related to the problem at hand, but is more focused on lane and obstacle detection rather than on path tracking, is described, for instance, in [35].…”

Section: Introductionmentioning

confidence: 99%

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

2015

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Localization and tracking systems are nowadays a quite common solution for automated guided vehicles (AGV) in industrial environments. The bunch of technological solutions developed in this sector is now being revitalized by their applications in service robots, e.g. for safe navigation in crowded public spaces or in autonomous cars. Related to this field are robotic competitions and races. In this particular scenario, the robot often has to track a line painted on the ground. Line tracking techniques typically rely on light dependent resistors (LDR), photo-diodes or photo-transistors detecting the light generated by normal or infrared Light Emitting Diodes (LEDs), arrays of electric inductance sensors or vision systems. Crucial issues for line tracking are: accuracy and reliability in estimating the direction of the moving vehicle with respect to the line and processing speed. Such problems are particularly critical when high-speed mobile robots are considered. To address this issue, in this paper a vision-based technique of moderate computational complexity is described.Â The proposed solution has been implemented on a low-cost embedded platform and relies on a high frame rate light contrast sensor, a tailored RANSAC-based algorithm and a Kalman filter. The reported experimental results prove that the proposed solution is able to track the direction of the vehicle in real-time even when the field of view of the camera is limited and the vehicle moves at high speed.

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A novel system for robust lane detection and tracking

Cited by 109 publications

References 20 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Lane Detection Algorithm for Night-time Digital Image Based on Distribution Feature of Boundary Pixels

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

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