loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Sahim Kourkouss ; Hideto Motomura ; Koichi Emura and Eriko Ohdachi

Affiliation: Panasonic Corporation, Japan

Keyword(s): Autonomous Driving, Self-Driving Cars, Driver Monitoring, Driver Behaviour, Deep Learning, Transfer Learning.

Abstract: Anticipating driving behaviours is a promising technology for novel advanced driver assistance systems. In recent years, predicting a driver’s future action became an important element to preventive safety technologies and has been advancing greatly contributing to a reduction in road accidents. In this paper, we propose a deep learning network that anticipates driving actions based on information of subject vehicle as well as surrounding vehicles and environment. By re-using a network trained on a great number of various drivers’ data with different driving behaviours and linking it to a particular driver with particular taste we propose a method that enables the anticipation of driving behaviours that can be tailored to each driver individually, leading to improved user experiences. We experimentally test our method for acceleration, deceleration and brake profile anticipation task using actual driving data. Our results demonstrate the effectiveness of our approach, achieving a gre at improvement when anticipating for individuals. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.149.214.32

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kourkouss, S.; Motomura, H.; Emura, K. and Ohdachi, E. (2018). Anticipating Driver Actions via Deep Neural Networks and New Driver Personalization Technique through Transfer Learning. In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-293-6; ISSN 2184-495X, SciTePress, pages 269-276. DOI: 10.5220/0006669002690276

@conference{vehits18,
author={Sahim Kourkouss. and Hideto Motomura. and Koichi Emura. and Eriko Ohdachi.},
title={Anticipating Driver Actions via Deep Neural Networks and New Driver Personalization Technique through Transfer Learning},
booktitle={Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2018},
pages={269-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006669002690276},
isbn={978-989-758-293-6},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Anticipating Driver Actions via Deep Neural Networks and New Driver Personalization Technique through Transfer Learning
SN - 978-989-758-293-6
IS - 2184-495X
AU - Kourkouss, S.
AU - Motomura, H.
AU - Emura, K.
AU - Ohdachi, E.
PY - 2018
SP - 269
EP - 276
DO - 10.5220/0006669002690276
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic