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Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data

Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural cros...

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Autores principales: Zhang, Hongjia, Liu, Yanjuan, Wang, Chang, Fu, Rui, Sun, Qinyu, Li, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147480/
https://www.ncbi.nlm.nih.gov/pubmed/32210116
http://dx.doi.org/10.3390/s20061776
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author Zhang, Hongjia
Liu, Yanjuan
Wang, Chang
Fu, Rui
Sun, Qinyu
Li, Zhen
author_facet Zhang, Hongjia
Liu, Yanjuan
Wang, Chang
Fu, Rui
Sun, Qinyu
Li, Zhen
author_sort Zhang, Hongjia
collection PubMed
description Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles.
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spelling pubmed-71474802020-04-20 Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data Zhang, Hongjia Liu, Yanjuan Wang, Chang Fu, Rui Sun, Qinyu Li, Zhen Sensors (Basel) Article Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles. MDPI 2020-03-23 /pmc/articles/PMC7147480/ /pubmed/32210116 http://dx.doi.org/10.3390/s20061776 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Hongjia
Liu, Yanjuan
Wang, Chang
Fu, Rui
Sun, Qinyu
Li, Zhen
Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
title Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
title_full Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
title_fullStr Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
title_full_unstemmed Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
title_short Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
title_sort research on a pedestrian crossing intention recognition model based on natural observation data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147480/
https://www.ncbi.nlm.nih.gov/pubmed/32210116
http://dx.doi.org/10.3390/s20061776
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