Assessment of the RSS Model Suitability using Graph Neural Network based on a Naturalistic Driving Dataset

Sungmoon Ahn, Shiho Kim

2022

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.

Paper Citation

in Harvard Style

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 - Volume 1: SIMULTECH, ISBN 978-989-758-578-4, pages 210-217. DOI: 10.5220/0011139800003274

in Bibtex Style

@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 - Volume 1: SIMULTECH,},
year={2022},
pages={210-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011139800003274},
isbn={978-989-758-578-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Assessment of the RSS Model Suitability using Graph Neural Network based on a Naturalistic Driving Dataset
SN - 978-989-758-578-4
AU - Ahn S.
AU - Kim S.
PY - 2022
SP - 210
EP - 217
DO - 10.5220/0011139800003274