Real-time lane detection and tracking for autonomous vehicle applications

Jiao, Xinyu; Yang, Daoguo; Jiang, Kun; Yu, Chunyan; Wen, Tuopu; Yan, Ruidong

doi:10.1177/0954407019866989

Cited by 16 publications

(12 citation statements)

References 21 publications

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“…As the rise of deep learning in the field of computer vision, increasing number of neural networks for lane line detection have been proposed [ 38 , 39 , 40 ]. Lane line detection algorithms based on deep learning can automatically extract the lane line features without making assumptions about the road structure and vehicle state, showing excellent robustness.…”

Section: Related Workmentioning

confidence: 99%

Lane-GAN: A Robust Lane Detection Network for Driver Assistance System in High Speed and Complex Road Conditions

Liu

Wang

et al. 2022

Micromachines

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Lane detection is an important and challenging part of autonomous driver assistance systems and other advanced assistance systems. The presence of road potholes and obstacles, complex road environments (illumination, occlusion, etc.) are ubiquitous, will cause the blur of images, which is captured by the vision perception system in the lane detection task. To improve the lane detection accuracy of blurred images, a network (Lane-GAN) for lane line detection is proposed in the paper, which is robust to blurred images. First, real and complex blur kernels are simulated to construct a blurred image dataset, and the improved GAN network is used to reinforce the lane features of the blurred image, and finally the feature information is further enriched with a recurrent feature transfer aggregator. Extensive experimental results demonstrate that the proposed network can get robust detection results in complex environments, especially for blurred lane lines. Compared with the SOTA detector, the proposed detector achieves a larger gain. The proposed method can enhance the lane detail features of the blurred image, improving the detection accuracy of the blurred lane effectively, in the driver assistance system in high speed and complex road conditions.

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Section: Related Workmentioning

confidence: 99%

Lane-GAN: A Robust Lane Detection Network for Driver Assistance System in High Speed and Complex Road Conditions

Liu

Wang

et al. 2022

Micromachines

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“…Jiao et al [14] created a real-time lane identification system based on a region of interest (ROI) that can function in noisy conditions and react quickly. A Kalman filter and a least-squares approximation of linear movement were used to track lines.…”

Section: Introductionmentioning

confidence: 99%

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

Noman

Zhai²,

Almukhtar³

et al. 2023

IJECE

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<span lang="EN-US">Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.</span>

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“…1. Simplified vanishing point detection method is employed in [1], and a scan-line method is applied to detect lane ridge features. Multi-LD algorithm that is robust to the challenging road conditions has been proposed in [2].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Ghanem

Kanungo

Panda

et al. 2021

Complex Intell. Syst.

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Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

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Real-time lane detection and tracking for autonomous vehicle applications

Cited by 16 publications

References 21 publications

Lane-GAN: A Robust Lane Detection Network for Driver Assistance System in High Speed and Complex Road Conditions

Lane-GAN: A Robust Lane Detection Network for Driver Assistance System in High Speed and Complex Road Conditions

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

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