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Authors: Sungmoon Ahn and Shiho Kim

Affiliation: School of Integrated Technology, Yonsei University, Incheon, 21983, Republic of Korea

Keyword(s): Neural Nets and Fuzzy Systems, Data Analytics and Simulation, Intelligent Transportation.

Abstract: We propose a method to evaluate the RSS model using data obtained from real roads. Recently, the Responsibility-Sensitive Safety (RSS) model representing the minimum safety distance has been proposed. After that, there were studies to evaluate the RSS model using simulators. Most virtual simulation studies showed that the RSS model guarantees safety but adversely affects traffic flow by estimating the distance too long than necessary. We evaluated the RSS model using data obtained in natural situational environments, unlike previous studies. First, we found correlations representing distances between vehicles from the data using Graph Neural Networks. Using the obtained correlations, we expressed it as a mathematical model through symbolic regression. As a result of comparing the model we found with the RSS model, we verified that the RSS model has a significant trade-off between safety and traffic flow.

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Paper citation in several formats:
Ahn, S. and Kim, S. (2022). Assessment of the RSS Model Suitability using Graph Neural Network based on a Naturalistic Driving Dataset. In Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-578-4; ISSN 2184-2841, SciTePress, pages 210-217. DOI: 10.5220/0011139800003274

@conference{simultech22,
author={Sungmoon Ahn and Shiho Kim},
title={Assessment of the RSS Model Suitability using Graph Neural Network based on a Naturalistic Driving Dataset},
booktitle={Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2022},
pages={210-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011139800003274},
isbn={978-989-758-578-4},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Assessment of the RSS Model Suitability using Graph Neural Network based on a Naturalistic Driving Dataset
SN - 978-989-758-578-4
IS - 2184-2841
AU - Ahn, S.
AU - Kim, S.
PY - 2022
SP - 210
EP - 217
DO - 10.5220/0011139800003274
PB - SciTePress

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