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## Scenario Analysis

Analyze the parameters and distribution of each scenario scene from the trajectory data. (Explain the cut-in scenario as an example).

#### Extraction of Targeted Scenario Scene

The extracted trajectory data includes the corresponding scene in the referenced scenarios [JAMA AD Framework]. Therefore, it was necessary to extract scenes corresponding to the scenario.
In this project, we have established method for extracting scenarios from trajectory data using rule-based algorithm.
The cut-in scenario will be used as an example to explain scenario scene extraction.

The figure shows the parameter definition of the cut-in scenario scene. Figure. Definition of required parameters (Cut-in Scenario)

Using the cut-in parameter definition and the following rules, we created an algorithm to extract the cut-in scenario scene.

• The cut-in vehicle’s lateral velocity remains constant in the same direction
• The other-vehicle enters the ego-vehicle’s lane from a contiguous lane
• Absence of vehicles between the other-vehicle and the ego-vehicle
• Start: The other-vehicle’s lateral velocity value increases from 0
(Positive value to the right of the vehicle)
• End: Lateral velocity returns to 0

The scenario is analyzed using the scenario extracted from the real traffic (trajectory) data from this algorithm.

#### Extraction of Parameter Values

The parameters of the cut-in scenario extracted from the real traffic (trajectory) data are organized. [Nakamura et al. 2021]
The figure shows the parameter distribution of cut-in scenario acquired between 2018 and 2020. Fig. Distribution of cut-in scenario parameter

#### Estimation of parameter distribution

The acquired real traffic data cannot drive comprehensively in Japan. Therefore, the distribution may be biased. [Nakamura et al. 2021]

Additional analysis was performed on the parameter distribution of the cut-in in real traffic, and the parameter distribution was estimated. [Nakamura et al. 2021]
• Estimating parameter distribution with β distribution (finite and continuous)
• Considering correlation between parameters
• Extrapolation for sensor range limitation and rare cases
• Estimation of multi-dimensional parameter distribution Fig. Estimation of parameter distribution (cut-in scenario)

In the SAKURA research project, these analysis results are continuously accumulated as evidence.