Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TITS.2019.2958624 http://hdl.handle.net/11449/195643 |
Resumo: | Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility. |
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Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal InformationSafetyVehiclesSpatiotemporal phenomenaPrediction algorithmsVehicle dynamicsHeuristic algorithmsRoadsVehicle routingvehicle safetyadvanced driver assistanceVehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP, BrazilUniv Bern, Inst Comp Sci & Appl Math, CH-3012 Bern, SwitzerlandState Univ Sao Paulo, Informat Sci Dept, BR-01049010 Sao Paulo, BrazilUniv Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, BrazilState Univ Sao Paulo, Informat Sci Dept, BR-01049010 Sao Paulo, BrazilFAPESP: 2018/19639-5FAPESP: 2019/24937-8Ieee-inst Electrical Electronics Engineers IncUniversidade Estadual de Campinas (UNICAMP)Univ BernUniversidade Estadual Paulista (Unesp)Universidade Federal de Minas Gerais (UFMG)Souza, Allan M. deBraun, TorstenBotega, Leonardo C. [UNESP]Villas, Leandro A.Loureiro, Antonio A. F.2020-12-10T17:58:54Z2020-12-10T17:58:54Z2020-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article3973-3989http://dx.doi.org/10.1109/TITS.2019.2958624Ieee Transactions On Intelligent Transportation Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 21, n. 9, p. 3973-3989, 2020.1524-9050http://hdl.handle.net/11449/19564310.1109/TITS.2019.2958624WOS:000564291100033Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Transactions On Intelligent Transportation Systemsinfo:eu-repo/semantics/openAccess2021-10-23T10:18:13Zoai:repositorio.unesp.br:11449/195643Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:30:08.529016Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information |
title |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information |
spellingShingle |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information Souza, Allan M. de Safety Vehicles Spatiotemporal phenomena Prediction algorithms Vehicle dynamics Heuristic algorithms Roads Vehicle routing vehicle safety advanced driver assistance |
title_short |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information |
title_full |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information |
title_fullStr |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information |
title_full_unstemmed |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information |
title_sort |
Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information |
author |
Souza, Allan M. de |
author_facet |
Souza, Allan M. de Braun, Torsten Botega, Leonardo C. [UNESP] Villas, Leandro A. Loureiro, Antonio A. F. |
author_role |
author |
author2 |
Braun, Torsten Botega, Leonardo C. [UNESP] Villas, Leandro A. Loureiro, Antonio A. F. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) Univ Bern Universidade Estadual Paulista (Unesp) Universidade Federal de Minas Gerais (UFMG) |
dc.contributor.author.fl_str_mv |
Souza, Allan M. de Braun, Torsten Botega, Leonardo C. [UNESP] Villas, Leandro A. Loureiro, Antonio A. F. |
dc.subject.por.fl_str_mv |
Safety Vehicles Spatiotemporal phenomena Prediction algorithms Vehicle dynamics Heuristic algorithms Roads Vehicle routing vehicle safety advanced driver assistance |
topic |
Safety Vehicles Spatiotemporal phenomena Prediction algorithms Vehicle dynamics Heuristic algorithms Roads Vehicle routing vehicle safety advanced driver assistance |
description |
Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T17:58:54Z 2020-12-10T17:58:54Z 2020-09-01 |
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 |
http://dx.doi.org/10.1109/TITS.2019.2958624 Ieee Transactions On Intelligent Transportation Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 21, n. 9, p. 3973-3989, 2020. 1524-9050 http://hdl.handle.net/11449/195643 10.1109/TITS.2019.2958624 WOS:000564291100033 |
url |
http://dx.doi.org/10.1109/TITS.2019.2958624 http://hdl.handle.net/11449/195643 |
identifier_str_mv |
Ieee Transactions On Intelligent Transportation Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 21, n. 9, p. 3973-3989, 2020. 1524-9050 10.1109/TITS.2019.2958624 WOS:000564291100033 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Transactions On Intelligent Transportation Systems |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
3973-3989 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128661679767552 |