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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information

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Autor(es):
de Souza, Allan M. [1, 2] ; Braun, Torsten [1] ; Botega, Leonardo C. [3] ; Villas, Leandro A. [2] ; Loureiro, Antonio A. F. [4]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Bern, Inst Comp Sci & Appl Math, CH-3012 Bern - Switzerland
[2] Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP - Brazil
[3] State Univ Sao Paulo, Informat Sci Dept, BR-01049010 Sao Paulo - Brazil
[4] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS; v. 21, n. 9, p. 3973-3989, SEPT 2020.
Citações Web of Science: 0
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. (AU)

Processo FAPESP: 19/24937-8 - Soluções para Sistema de Transporte Inteligentes e Cooperativos baseados em Computação Urbana
Beneficiário:Allan Mariano de Souza
Linha de fomento: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
Processo FAPESP: 18/19639-5 - Soluções para sistemas de transporte inteligentes e cooperativos baseados na computação urbana
Beneficiário:Leandro Aparecido Villas
Linha de fomento: Auxílio à Pesquisa - Regular