## 4 Experiments and Analysis
### Model Comparison Experiment
#### 4.4.2 Comparison of Model Efficiency and Complexity  
To comprehensively evaluate the practical performance of our model in dangerous driving behavior detection, we conducted experiments on the SfdDD dataset, comparing YOLO-AFR with several representative models, including YOLO-SGC, YOLOv10n, YOLOv11n, and YOLOv12n. The comparison covers key performance metrics such as computational complexity (GFLOPs), number of parameters, inference speed, frames per second (FPS), and model weight size. The results are shown in Table 8.  
As illustrated in the table, YOLO-AFR demonstrates significant advantages across multiple dimensions. It has the lowest computational complexity (only 5.7 GFLOPs), a compact parameter size of 2.421 M, and achieves an inference speed of 104.59 FPS, with a weight size of just 5.0 MB. This allows YOLO-AFR to maintain high accuracy while delivering exceptional efficiency and deployment flexibility. These performance metrics are especially critical in the domain of dangerous driving behavior detection. Traffic scenarios are often highly dynamic, requiring models with high \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metric** & **Group** & **N** & **Mean** & **SD** & \(t\)**-Test** & **[PERSON]’s \(t\)-Test** & **Mean Diff.** & **[PERSON]’s d** \\ \hline \multirow{2}{*[EMAIL_ADDRESS]} & YOLO-SGC & 20 & 0.969 & 0.003 & T = \(-\)6.953 & T = \(-\)6.953 & \\  & YOLO-AFR & 20 & 0.973 & 0.001 & \(p<0.001\) & \(p<0.001\) & 0.004 & 2.199 \\ \hline \multirow{2}{*[EMAIL_ADDRESS]} & Total & 40 & 0.971 & 0.003 & & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Statistical Comparison of [EMAIL_ADDRESS] for YOLO-SGC and YOLO-AFR on YawDD-E Dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** &
\begin{tabular}{c} [PERSON] \\ et.al. [45] \\ \end{tabular} & [PERSON] et al. [46] & [PERSON] et al. [47] & [PERSON] et al. [48] & [PERSON] et al. [49] & [PERSON] et al. [50] & [PERSON] et al. [51] & YOLO-SGC [17] & YOLO-AFR \\ \hline
**[EMAIL_ADDRESS]** & 0.818 & 0.834 & 0.880 & 0.910 & 0.934 & 0.960 & 0.944 & 0.972 & **0.976** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [EMAIL_ADDRESS] Comparison of Different Detection Methods on the YawDD Dataset. The top results for each metric are shown in bold, with the next best results underlined. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metric** & **Group** & **N** & **Mean** & **SD** & \(t\)**-Test** & **[PERSON]’s \(t\)-Test** & **Mean Diff.** & **Cohen’s d** \\ \hline \multirow{2}{*[EMAIL_ADDRESS]} & YOLO-SGC & 20 & 0.969 & 0.003 & T = \(-\)6.953 & T = \(-\)6.953 & \\  & YOLO-AFR & 20 & 0.973 & 0.001 & \(p<0.001\) & \(p<0.001\) & 0.004 & 2.199 \\ \hline \multirow{2}{*[EMAIL_ADDRESS]} & Total & 40 & 0.971 & 0.003 & & & & & \\ \hline \end{tabular}
\end{table} 
Table 7: Statistical Comparison of [EMAIL_ADDRESS] for YOLO-SGC and YOLO-AFR on SfdDD Dataset \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** &
\begin{tabular}{c} [PERSON] \\ et al. [52] \\ \end{tabular} & [PERSON] et al. [53] & [PERSON] et al. [54] & [PERSON] et al. [55] & [PERSON] et al. [56] & [PERSON] et al. [57] & [PERSON] et al. [58] & YOLO-SGC [17] & YOLO-AFR \\ \hline
**[EMAIL_ADDRESS]** & 0.937 & 0.952 & 0.956 & 0.949 & 0.946 & 0.935 & 0.976 & 0.980 & **0.989** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: [EMAIL_ADDRESS] Comparison of Different Detection Methods on the SfdDD Dataset. The top results for each metric are shown in bold, with the next best results underlined. frame rates and low latency to ensure timely alerts. Additionally, lightweight models are essential for deployment on in-vehicle or edge devices. By striking a balance between accuracy, speed, and resource efficiency, YOLO-AFR proves to be highly suitable and powerful for real-world driving safety applications.