Safe and Sound: Driver Safety-Aware Vehicle Re-Rou... - BV FAPESP
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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
de Souza, Allan M. [1, 2] ; Braun, Torsten [1] ; Botega, Leonardo C. [3] ; Villas, Leandro A. [2] ; Loureiro, Antonio A. F. [4]
Total Authors: 5
Affiliation:
[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
Total Affiliations: 4
Document type: Journal article
Source: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS; v. 21, n. 9, p. 3973-3989, SEPT 2020.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 19/24937-8 - Solutions for Intelligent and Cooperative Transportation Systems based on Urban Computing
Grantee:Allan Mariano de Souza
Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
FAPESP's process: 18/19639-5 - Solutions for intelligent and cooperative transportation systems based on urban computing
Grantee:Leandro Aparecido Villas
Support Opportunities: Regular Research Grants