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Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap
Numerous traffic crashes occur every year on zebra crossings in China. Pedestrians are vulnerable road users who are usually injured severely or fatally during human-vehicle collisions. The development of an effective pedestrian street-crossing decision-making model is essential to improving pedestr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763216/ https://www.ncbi.nlm.nih.gov/pubmed/33321945 http://dx.doi.org/10.3390/ijerph17249247 |
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author | Zhang, Hongjia Guo, Yingshi Chen, Yunxing Sun, Qinyu Wang, Chang |
author_facet | Zhang, Hongjia Guo, Yingshi Chen, Yunxing Sun, Qinyu Wang, Chang |
author_sort | Zhang, Hongjia |
collection | PubMed |
description | Numerous traffic crashes occur every year on zebra crossings in China. Pedestrians are vulnerable road users who are usually injured severely or fatally during human-vehicle collisions. The development of an effective pedestrian street-crossing decision-making model is essential to improving pedestrian street-crossing safety. For this purpose, this paper carried out a naturalistic field experiment to collect a large number of vehicle and pedestrian motion data. Through interviewed with many pedestrians, it is found that they pay more attention to whether the driver can safely brake the vehicle before reaching the zebra crossing. Therefore, this work established a novel decision-making model based on the vehicle deceleration-safety gap (VD-SGM). The deceleration threshold of VD-SGM was determined based on signal detection theory (SDT). To verify the performance of VD-SGM proposed in this work, the model was compared with the Raff model. The results show that the VD-SGM performs better and the false alarm rate is lower. The VD-SGM proposed in this work is of great significance to improve pedestrians’ safety. Meanwhile, the model can also increase the efficiency of autonomous vehicles. |
format | Online Article Text |
id | pubmed-7763216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77632162020-12-27 Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap Zhang, Hongjia Guo, Yingshi Chen, Yunxing Sun, Qinyu Wang, Chang Int J Environ Res Public Health Article Numerous traffic crashes occur every year on zebra crossings in China. Pedestrians are vulnerable road users who are usually injured severely or fatally during human-vehicle collisions. The development of an effective pedestrian street-crossing decision-making model is essential to improving pedestrian street-crossing safety. For this purpose, this paper carried out a naturalistic field experiment to collect a large number of vehicle and pedestrian motion data. Through interviewed with many pedestrians, it is found that they pay more attention to whether the driver can safely brake the vehicle before reaching the zebra crossing. Therefore, this work established a novel decision-making model based on the vehicle deceleration-safety gap (VD-SGM). The deceleration threshold of VD-SGM was determined based on signal detection theory (SDT). To verify the performance of VD-SGM proposed in this work, the model was compared with the Raff model. The results show that the VD-SGM performs better and the false alarm rate is lower. The VD-SGM proposed in this work is of great significance to improve pedestrians’ safety. Meanwhile, the model can also increase the efficiency of autonomous vehicles. MDPI 2020-12-10 2020-12 /pmc/articles/PMC7763216/ /pubmed/33321945 http://dx.doi.org/10.3390/ijerph17249247 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 Guo, Yingshi Chen, Yunxing Sun, Qinyu Wang, Chang Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap |
title | Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap |
title_full | Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap |
title_fullStr | Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap |
title_full_unstemmed | Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap |
title_short | Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap |
title_sort | analysis of pedestrian street-crossing decision-making based on vehicle deceleration-safety gap |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763216/ https://www.ncbi.nlm.nih.gov/pubmed/33321945 http://dx.doi.org/10.3390/ijerph17249247 |
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