A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information
The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly de...
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doaj-e28a1f6fbcdd48d99d578c81b275b9bb2021-01-22T00:01:26ZengMDPI AGSensors1424-82202021-01-012170870810.3390/s21030708A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving InformationWenbo Liu0Fei Yan1Jiyong Zhang2Tao Deng3School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, ChinaThe quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.https://www.mdpi.com/1424-8220/21/3/708lane detectionvertical spatial featurescontextual informationcomplex driving scenes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wenbo Liu Fei Yan Jiyong Zhang Tao Deng |
spellingShingle |
Wenbo Liu Fei Yan Jiyong Zhang Tao Deng A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information Sensors lane detection vertical spatial features contextual information complex driving scenes |
author_facet |
Wenbo Liu Fei Yan Jiyong Zhang Tao Deng |
author_sort |
Wenbo Liu |
title |
A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information |
title_short |
A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information |
title_full |
A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information |
title_fullStr |
A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information |
title_full_unstemmed |
A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information |
title_sort |
robust lane detection model using vertical spatial features and contextual driving information |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-01-01 |
description |
The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios. |
topic |
lane detection vertical spatial features contextual information complex driving scenes |
url |
https://www.mdpi.com/1424-8220/21/3/708 |
work_keys_str_mv |
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