Lane Detection and Tracking For Intelligent Vehicles: A Survey

Feniche, Mehdi; Mazri, Tomader

doi:10.1109/iccsre.2019.8807727

Cited by 23 publications

(9 citation statements)

References 18 publications

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“…One is the use of classic methods in machine vision, and another is the adoption of deep learning methods. Many pieces of research have already been conducted on comparison review and stating the advantages and disadvantages of the proposed methods for road data extraction and lane detection [10][11][12][13][14]. Here we also deal with some methods that employ deep learning for road and its lanes detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for Automatic Lane Detection using a Deep Network

2022

IJE

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Lane detection is amongst the most important operations during the automatic driving process. This process aims to detect lane lines to control the vehicle's direction in a specific lane on the road and it can be effective in preventing accidents. Besides being online, the most requirement for lane detection methods in automatic driving is their high accuracy. The use of deep learning to create fully automated systems in lane detection has been done extensively. Automated learning-based methods used for road lane detection are often of supervised type. One of the disadvantages of these methods, despite their excellent accuracy, is that they need a set of labeled data, which limits the development procedure of the lane detection system, and most importantly establishing a standard set of labeled data is very timedemanding. The recommended solution is to use appropriate learning approaches that can be used to achieve the relative accuracy of the supervised approaches and to improve their speed. It also enables us to use different datasets without a constraint label that the tagged dataset would create in the algorithm development relative to the new dataset. In this research, we present an automatic semi-supervised learning method using deep neural networks to extract the data of lane lines, using labeled (mask for detected lines) and unlabeled datasets. The results demonstrate the suitable accuracy of the adopted method according to the proposed approach and also improves its computational complexity due to the significant reduction in the number of teachable network parameters.

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Section: Literature Reviewmentioning

confidence: 99%

A Method for Automatic Lane Detection using a Deep Network

2022

IJE

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“…Traditional lane line detection methods generally separate lane lines from non-lane line areas on the road surface through clustering or segmentation according to the characteristic information such as lane line color, edge, or gradient change ( 12 ). The road surface and lane markings can be distinguished by counting the range of road surface pixels in the road surface area according to the RGB (red, green, blue) information of road surface pixels ( 13 , 14 ). The edge information of the lane line is also an important feature.…”

Section: Related Workmentioning

confidence: 99%

Lane Lines Detection under Complex Environment by Fusion of Detection and Prediction Models

Haris

Hou

Wang

2021

Transportation Research Record

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The lane lines’ length, width, and direction are very regular, serialized, and structurally associated, which are not easily affected by the environment. To enhance lane detection in a complicated environment, an approach combines visual information with the spatial distribution. Firstly, the grid density of the target detection algorithm YOLOv3 (you only look once V3) is improved from S×S to S×2S, aiming at the particular points in the bird’s-eye view where the lane lines had different densities in the horizontal and vertical directions. The obtained YOLOv3 (S×2S) is more suitable for detecting objects with small and large aspect ratios. It also identifies image features along with balances the detection speed and accuracy. Secondly, based on a bi-directional gated recurrent unit (BGRU), a new lane line prediction model BGRU-Lane (BGRU-L) based on the distribution of lane lines is proposed using the characteristic of lane line serialization and structural correlation. Finally, Dempster-Shafer (D-S) algorithm based on confidence was used to integrate the results of YOLOv3 (S×2S) and BGRU-L to improve the lane line detection ability under complex environments. The experiment was carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset, while Euro Truck Simulator 2 (ETS2) is used as a supplement dataset. After fusing YOLOv3 (S×2S) and BGRU-L models in the D-S model, the detection results have high accuracy in a complex environment by 90.28 mAP. The detection speed is 40.20fps, which enables real-time detection.

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“…Pada penelitian "Aplikasi Pendeteksi Rambu Lalu-Lintas Menggunakan Operator Sobel dan Metode Hamming", dengan adanya aplikasi ini diharapkan dapat mempermudah user dalam mengetahui fungsi dari rambu-rambu yang terdapat di jalan raya [3]. Pada penelitian "Lane Detection and Tracking For Intelligent Vehicles: A Survey", hasil dari penelitian ini adalah bahwa pada beberapa model yang digunakan untuk Lane Detection and Tracking harus mempertimbangkan beberapa faktor, seperti garis lalu lintas dan rambu lalu lintas, serta mempertimbangkan sisi kompleksitas dan effisiensi dari algoritma dan juga big data [4]. Penelitan lainnya "A Study on Detection Method of Vehicle Based on Lane Detection for a Driver Assistance System Using a Camera on Highway" hasil penelitianya adalah algoritma bekerja pada rata rata 42 milliseconds pada setiap frame dengan resolusi 3.30 intel CPU [5].…”

Section: Pendahuluanunclassified

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

Salkiawati

Alexander

Lubis

2021

mib

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Based on the traffic accident report, it was found that there were 41,771 (Forty-one thousand seven hundred and seventy-one) incidents caused by disorderly drivers. (POLRI, 2018). One of these disorders is by driving a motorized vehicle outside the traffic lane. In this study, researchers developed computer vision using sensor methods and image processing. The stages in computer vision are the image acquisition process, the image segmentation process, and the image understanding process. This study aims to develop an application using computer vision to warn drivers of disorderly traffic or to increase the alertness of motorized vehicle drivers by detecting the condition of the driver's path. It is hoped that this research will provide a sense of security for motorized vehicle drivers, as well as provide applications that are expected to increase driver awareness to avoid traffic accidents

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Lane Detection and Tracking For Intelligent Vehicles: A Survey

Cited by 23 publications

References 18 publications

A Method for Automatic Lane Detection using a Deep Network

A Method for Automatic Lane Detection using a Deep Network

Lane Lines Detection under Complex Environment by Fusion of Detection and Prediction Models

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

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