Deep learning approaches for vulnerable road users safety

Gwanggil Jeon; Valerio Bellandi; Abdellah Chehri; Ernesto Damiani

doi:10.1016/j.ssci.2022.105964

Deep learning approaches for vulnerable road users safety

Gwanggil Jeon
, Valerio Bellandi
, Abdellah Chehri
, Ernesto Damiani

Electrical Engineering

Incheon National University
Università degli Studi di Milano
Royal Military College of Canada

Research output: Contribution to journal › Editorial

2 Scopus citations

Original language	British English
Article number	105964
Journal	Safety Science
Volume	158
DOIs	https://doi.org/10.1016/j.ssci.2022.105964
State	Published - Feb 2023

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@article{6f1ee54df4574f64a2670e356c07faaa,

title = "Deep learning approaches for vulnerable road users safety",

author = "Gwanggil Jeon and Valerio Bellandi and Abdellah Chehri and Ernesto Damiani",

note = "Funding Information: We want to express our gratitude to all the authors for their informative articles and the reviewers for their constructive critiques and support in organizing this special issue possible. Finally, we want to express our sincere gratitude to Professor Georgios Boustras, the Editor in Chief, for giving us with this unique opportunity to provide our works in Safety Science.",

year = "2023",

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doi = "10.1016/j.ssci.2022.105964",

language = "British English",

volume = "158",

journal = "Safety Science",

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TY - JOUR

T1 - Deep learning approaches for vulnerable road users safety

AU - Jeon, Gwanggil

AU - Bellandi, Valerio

AU - Chehri, Abdellah

AU - Damiani, Ernesto

N1 - Funding Information: We want to express our gratitude to all the authors for their informative articles and the reviewers for their constructive critiques and support in organizing this special issue possible. Finally, we want to express our sincere gratitude to Professor Georgios Boustras, the Editor in Chief, for giving us with this unique opportunity to provide our works in Safety Science.

PY - 2023/2

Y1 - 2023/2

UR - https://www.scopus.com/pages/publications/85140767635

U2 - 10.1016/j.ssci.2022.105964

DO - 10.1016/j.ssci.2022.105964

M3 - Editorial

AN - SCOPUS:85140767635

SN - 0925-7535

VL - 158

JO - Safety Science

JF - Safety Science

M1 - 105964

ER -

Deep learning approaches for vulnerable road users safety

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