Using machine learning on V2X communications data for VRU collision prediction

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Bruno
Data de Publicação: 2023
Outros Autores: Nicolau, Maria João, Santos, Alexandre
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/85377
Resumo: The datasets presented in this study are available in Zenodo at https://doi.org/10.5281/zenodo.7376770 (accessed on 16 December 2022), reference number [23]. These datasets are the raw data used for the testing and training of the ML algorithms in this work.
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spelling Using machine learning on V2X communications data for VRU collision predictionVehicular communicationsVulnerable road usersCollision predictionMachine learningEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaScience & TechnologyThe datasets presented in this study are available in Zenodo at https://doi.org/10.5281/zenodo.7376770 (accessed on 16 December 2022), reference number [23]. These datasets are the raw data used for the testing and training of the ML algorithms in this work.Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equipped with communication capabilities, the generated Vehicle-to-Anything (V2X) data can be leveraged by Machine Learning (ML) mechanisms in order to implement such automatic systems. This work proposes a VRU (motorcyclist) collision prediction system, utilizing stacked unidirectional Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (coupling the Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3) tools). The proposed system performed well in two different scenarios: in Scenario A, it predicted 96% of the collisions, averaging 4.53 s for Average Prediction Time (s) (APT) and with a Correct Decision Percentage (CDP) of 41% and 78 False Positives (FPs); in Scenario B, it predicted 95% of the collisions, with a 4.44 s APT, while the CDP was 43% with 68 FPs. The results show the effectiveness of the approach: using ML methods on V2X data allowed the prediction of most of the simulated accidents. Nonetheless, the presence of a relatively high number of FPs does not allow for the usage of <i>automatic</i> safety features (e.g., emergency breaking in the passenger vehicles); thus, collision avoidance must be achieved <i>manually</i> by the drivers.This work has been supported by national funds through FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/00319/2020.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoRibeiro, BrunoNicolau, Maria JoãoSantos, Alexandre2023-01-222023-01-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85377engRibeiro, B.; Nicolau, M.J.; Santos, A. Using Machine Learning on V2X Communications Data for VRU Collision Prediction. Sensors 2023, 23, 1260. https://doi.org/ 10.3390/s230312601424-82201424-822010.3390/s2303126036772299https://www.mdpi.com/1424-8220/23/3/1260info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T11:58:24Zoai:repositorium.sdum.uminho.pt:1822/85377Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:48:07.162031Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Using machine learning on V2X communications data for VRU collision prediction
title Using machine learning on V2X communications data for VRU collision prediction
spellingShingle Using machine learning on V2X communications data for VRU collision prediction
Ribeiro, Bruno
Vehicular communications
Vulnerable road users
Collision prediction
Machine learning
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Science & Technology
title_short Using machine learning on V2X communications data for VRU collision prediction
title_full Using machine learning on V2X communications data for VRU collision prediction
title_fullStr Using machine learning on V2X communications data for VRU collision prediction
title_full_unstemmed Using machine learning on V2X communications data for VRU collision prediction
title_sort Using machine learning on V2X communications data for VRU collision prediction
author Ribeiro, Bruno
author_facet Ribeiro, Bruno
Nicolau, Maria João
Santos, Alexandre
author_role author
author2 Nicolau, Maria João
Santos, Alexandre
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Ribeiro, Bruno
Nicolau, Maria João
Santos, Alexandre
dc.subject.por.fl_str_mv Vehicular communications
Vulnerable road users
Collision prediction
Machine learning
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Science & Technology
topic Vehicular communications
Vulnerable road users
Collision prediction
Machine learning
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Science & Technology
description The datasets presented in this study are available in Zenodo at https://doi.org/10.5281/zenodo.7376770 (accessed on 16 December 2022), reference number [23]. These datasets are the raw data used for the testing and training of the ML algorithms in this work.
publishDate 2023
dc.date.none.fl_str_mv 2023-01-22
2023-01-22T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/85377
url https://hdl.handle.net/1822/85377
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ribeiro, B.; Nicolau, M.J.; Santos, A. Using Machine Learning on V2X Communications Data for VRU Collision Prediction. Sensors 2023, 23, 1260. https://doi.org/ 10.3390/s23031260
1424-8220
1424-8220
10.3390/s23031260
36772299
https://www.mdpi.com/1424-8220/23/3/1260
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799132241912135680

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