lane line
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2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Cheng Xu ◽  
Hongjun Wu ◽  
Yinong Zhang ◽  
Songyin Dai ◽  
Hongzhe Liu ◽  
...  

The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.


2021 ◽  
Vol 50 (4) ◽  
pp. 722-735
Author(s):  
W. Wang ◽  
F. Berholm ◽  
K. Hu ◽  
L. Zhao ◽  
S. Feng ◽  
...  

To accurately detect lane lines in road traffic images at raining weather, a edge detection based method is studied, which mainly includes four algorithms. (1) Firstly an image is enhanced by an improved Retinex algorithm; (2) Then, an algorithm based on the Hessian matrix is applied to strengthen lane lines; (3) To extract the feature points of a lane line, a ridge edge detection algorithm based on five line detection in four directions is proposed, in which, in light on the possible positions of lane lines in the image, it detects the maximum gray level points in the local area of the detecting point within the pre-set valid detection region; and (4) After the noise removal based on the minimum circumscribed rectangles, the candidate points of lane lines are connected as segments, and for the gap filling between segments, in order to make connection correctly, the algorithm makes the filling in two steps, short gap and long gap fillings, and the long gap filling is made on the combination of segment angle difference and gap distance and gap angle. By testing hundreds of images of the lane lines at raining weather and by comparing several traditional image enhancement and segmentation algorithms, the new method of the lane line detection can produce the satisfactory results.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kang Liu ◽  
Xin Gao

The use of multimodal sensors for lane line segmentation has become a growing trend. To achieve robust multimodal fusion, we introduced a new multimodal fusion method and proved its effectiveness in an improved fusion network. Specifically, a multiscale fusion module is proposed to extract effective features from data of different modalities, and a channel attention module is used to adaptively calculate the contribution of the fused feature channels. We verified the effect of multimodal fusion on the KITTI benchmark dataset and A2D2 dataset and proved the effectiveness of the proposed method on the enhanced KITTI dataset. Our method achieves robust lane line segmentation, which is 4.53% higher than the direct fusion on the precision index, and obtains the highest F2 score of 79.72%. We believe that our method introduces an optimization idea of modal data structure level for multimodal fusion.


2021 ◽  
Vol 11 (22) ◽  
pp. 10713
Author(s):  
Dong-Gyu Lee

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.


Author(s):  
Zhenhong Zou ◽  
Xinyu Zhang ◽  
Huaping Liu ◽  
Zhiwei Li ◽  
Amir Hussain ◽  
...  

2021 ◽  
Vol 22 (4) ◽  
pp. 461-470
Author(s):  
Jozsef Suto

Abstract Autonomous navigation is important not only in autonomous cars but also in other transportation systems. In many applications, an autonomous vehicle has to follow the curvature of a real or artificial road or in other words lane lines. In those application, the key is the lane detection. In this paper, we present a real-time lane line tracking algorithm mainly designed to mini vehicles with relatively low computation capacity and single camera sensor. The proposed algorithm exploits computer vision techniques in combination with digital filtering. To demonstrate the performance of the method, experiments are conducted in an indoor, self-made test track where the effect of several external influencing factors can be observed. Experimental results show that the proposed algorithm works well independently of shadows, bends, reflection and lighting changes.


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