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

: 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.