An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications
One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pe...
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2021-01-01
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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2021/6621451 |
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doaj-20f0edad1c644e2992c1d46def1b4d152021-02-15T12:52:48ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772021-01-01202110.1155/2021/66214516621451An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing ApplicationsShicai Ji0Ying Peng1Hongjia Zhang2Shengbo Wu3School of Vehicle Engineering, Shandong Transport Vocational College, Weifang, Shandong 261206, ChinaSchool of Vehicle Engineering, Shandong Transport Vocational College, Weifang, Shandong 261206, ChinaSchool of Automobile, Chang’an University, Xi’an, Shaanxi 710064, ChinaSchool of Automobile, Chang’an University, Xi’an, Shaanxi 710064, ChinaOne of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS3VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S3VM, and OS3VM indicate that the proposed OS3VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S3VM models, and the accuracy achieves 94.83%. Therefore, the OS3VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy.http://dx.doi.org/10.1155/2021/6621451 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shicai Ji Ying Peng Hongjia Zhang Shengbo Wu |
spellingShingle |
Shicai Ji Ying Peng Hongjia Zhang Shengbo Wu An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications Wireless Communications and Mobile Computing |
author_facet |
Shicai Ji Ying Peng Hongjia Zhang Shengbo Wu |
author_sort |
Shicai Ji |
title |
An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications |
title_short |
An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications |
title_full |
An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications |
title_fullStr |
An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications |
title_full_unstemmed |
An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications |
title_sort |
online semisupervised learning model for pedestrians’ crossing intention recognition of connected autonomous vehicle based on mobile edge computing applications |
publisher |
Hindawi-Wiley |
series |
Wireless Communications and Mobile Computing |
issn |
1530-8669 1530-8677 |
publishDate |
2021-01-01 |
description |
One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS3VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S3VM, and OS3VM indicate that the proposed OS3VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S3VM models, and the accuracy achieves 94.83%. Therefore, the OS3VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy. |
url |
http://dx.doi.org/10.1155/2021/6621451 |
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