# Quantitative Testing and Analysis of Non-Standard AEB Scenarios Extracted from Corner Cases
## 3 Simulation Scenario Construction and Testing
### Scenario Test and Analysis  
Our proposed methods will be discussed here, including 1. Data Missing Estimation (DME) Scene architecture method, 2. C-AEB (curve AEB) Test method, 3. Boundary collision evaluation model (BCEM) Evaluation model. We proposed the DME (Data Missing Estimation) method for scene construction, which addresses the lack of accurate data on the positions and distances of relevant traffic participants in the natural driving dataset. Additionally, we propose the C-AEB (Curve-AEB) model for non-standard AEB testing in curved road scenarios and the BCEM (Boundary collision evaluation model) for assessing non-standard AEB performance specifically in curved road scenarios. Subsequently, we create the scenarios in PRESCAN and develop \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Warning Flag** & **1.6 s Flag** & **0.6 s Flag** & **Action** \\ \hline
0 & 0 & 1 & Do nothing \\ \hline
0 & 1 & 0 & Apply 40\% braking force \\ \hline
0 & 1 & 1 & Apply Max braking force \\ \hline
1 & 0 & 1 & Apply braking force put on by the driver \\ \hline
1 & 1 & 0 & Apply 40\% braking force \\ \hline
1 & 1 & 1 & Apply Max braking force \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AEB actions of different warning signals. the AEB system using SIMULINK in MATLAB. Finally, we conduct simulation testing and perform evaluation analysis. 1. DME (Data Missing Estimation) Scene architecture method  
Here, we will construct the selected image scenes in PRESCAN and develop an experimental plan. Since dangerous scenarios are continuous in time, and the natural driving dataset lacks accurate data on the positions and distances of relevant traffic participants, the focus of the scenario testing is on the performance of the AEB system in this scenario rather than the performance of a specific test case within the scenario. Therefore, we assume that the positions of the ego vehicle and obstacle vehicles within the feasible driving area are uniformly distributed in the same scenario. Drawing inspiration from the commonly used 5-point sampling method in biology, we propose a \"Data Missing Estimation\" for AEB system testing to test the overall scenario. The specific approach is as follows:  
For scenarios where the ego vehicle and obstacle vehicle intersect (such as at intersections or merging lanes), we will use the intersection point of the lane centerlines as the collision center point. Two collision points will be arranged equidistantly on either side of the collision center point, resulting in a total of five collision points (the collision point here refers to the coordinate point at which the car collides with the car in front of it in the current scene). For curved road scenarios, since the trajectories of the ego vehicle and obstacle vehicle overlap, we will divide the curved road into five segments and place a collision point at the center of each segment, resulting in a total of five collision points. For straight road scenarios, we will set five collision points at equal distances along the road. 2. C-AEB (curve AEB) Test method  
Based on the CN-cap testing standard, we propose a testing methodology specifically designed for curved road scenarios, called C-AEB (Curve-Aware Emergency Braking). In this methodology, the test cases for each scenario are divided into three categories: Lead Vehicle Stationary (CCRs), Lead Vehicle Slow Moving (CCRm), and Lead Vehicle Decelerating (CCRb). Unlike the testing standard, the Lead Vehicle Stationary category involves the lead vehicle being stationary at five different points. The test vehicle starts from 0 km/h and incrementally increases its speed by 5 km/h until a collision occurs. In the Lead Vehicle Slow Moving category, the lead vehicle travels at speeds of 10 km/h, 20 km/h, and 30 km/h. The starting position of the test vehicle is not fixed but remains outside the perception range. The final collision point is set at the aforementioned five points. For the Lead Vehicle Decelerating category, the setup is similar to the Lead Vehicle Slow Moving category.