# Quantitative Testing and Analysis of Non-Standard AEB Scenarios Extracted from Corner Cases
## 2 Extraction of Dangerous Scenes
### Extraction of Dangerous Scenarios Based on Natural Driving Data  
The method proposed by [PERSON] [28] and [PERSON] et al. [25] for screening hazardous driving conditions from naturalistic driving data was adopted in this study. The data were screened for hazardous driving conditions along both longitudinal and lateral dimensions, as shown in Table 1.  
The method route for scenario screening is as follows (Figure 1):  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Trigger Class** & & **Description** \\ \hline \multirow{3}{*}{Longitudinal condition} & Longitudinal deceleration & The longitudinal deceleration is less than or equal to \(-5\) m/s\({}^{2}\) \\  & Longitudinal acceleration & The longitudinal acceleration is greater than or equal to \\  & Longitudinal deceleration gradient & The velocity is higher than \(5\) m/s, and the longitudinal \\  & Lateral acceleration & The absolute value is greater than \(7\) m/s\({}^{2}\) \\  & Lateral acceleration derivative & If the velocity is greater than \(5\) m/s, the absolute derivative of \\  & & the lateral acceleration is greater than or equal to \(10\) m/s\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Screening conditions for dangerous conditions.  
Figure 1: Scene screening method.  
The deceleration gradient can be used to measure the change in acceleration and, consequently, the speed at which the vehicle's motion state changes. In the event of an emergency, the vehicle's motion state undergoes significant changes within a short period. Here, the longitudinal deceleration gradient is primarily selected as the main screening criterion for hazardous scenarios.  
The IMU data (IMU Data is the data collected by IMU sensors) in the KITTI dataset records the vehicle's motion information at each timestamp, including dimensions, longitude, altitude, roll angle, pitch angle, yaw angle, longitudinal velocity, lateral velocity, \(x\)-axis acceleration, \(y\)-axis acceleration, and \(z\)-axis acceleration. According to the query results, the collection frequency of the KITTI dataset is 10 Hz, that is, the interval between every two timestamps is 0.1 s, from which the acceleration gradient corresponding to each timestamp can be calculated.  
The KITTI dataset contains five folders: 2011_09_26, 2011_09_28, 2011_09_29, 2011_09_30, and 2011_10_03. Taking the 2011_09_26 dataset as an example, the screening was performed, and Figure 2 shows the screening results of the 2011_09_26 dataset.  
Figure 2: Distribution of \(V_{x}\) and \(\Delta a_{n}\) in data set 2011_09_26 (\(V_{x}\) refers to the longitudinal speed of the vehicle, \(\Delta a_{x}\) refers to the rate of change of longitudinal acceleration of the vehicle. The blue lines refer to the velocity and acceleration corresponding to each timestamp in the data set, the red lines refer to the kinematic conditions, and the direction of the arrows refers to the points that satisfy the data).  
The analysis results indicate that there are hazardous scenarios in this dataset that meet the longitudinal conditions. Detailed analysis was conducted for each sub-dataset, and the corresponding images were located to identify the corresponding scenarios.  
Setting \(\Delta a_{x}<-10\) and \(V_{x}>5\) as the criteria, screening was performed on the above five datasets, and the potential hazardous scenarios in this dataset were identified, as shown in Table 2:  
Each data set is located through the time stamp, and after analysis, data from groups 4, 5, 6, 7, and 8 are eliminated. The corresponding scene has no traffic participants and belongs to the acceleration gradient change caused by terrain change.  
Retrieve the images corresponding to sequence 1 based on the timestamp and collect all the images captured 5 s before and 5 s after the corresponding timestamp to reconstruct the scene. The image frames corresponding to the selected scenes are shown in Figures 3-5.