## 4 Experimental Analysis
### Vehicle Recognition Accuracy  
Vehicle recognition training was conducted on the KITTI dataset. This dataset is co-sponsored by the Karlsruhe Institute of Technology in Germany and Toyota Technological Institute at Chicago, specifically for research in autonomous driving. The dataset comprises 7480 images, categorized into three labels: vehicles, pedestrians, and bicycles. This is partitioned into training, validation, and test sets in an 8:1:1 ratio.  
During the training process, the settings are detailed in Table 3.  
This section focuses on the recognition of vehicle categories. After training, the model achieved a vehicle recognition accuracy of 0.894, a recall rate of 0.882, an mAP@50 of 0.938, and an mAP@50-95 of 0.735. These results demonstrate that the model performed robustly in vehicle recognition, accurately and comprehensively detecting most instances.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Item** & **Configuration Information** \\ \hline Operating system & Windows 10 \\ Development language & Python 3.7.16 \\ CPU & 11 th Gen Intel(R) Core (TM) i5-11400H @ 2.70 GHz 2.69 GHz \\ GPU & Intel(R) UHD Graphics/NVIDIA GeForce RTX 3050 Ti Laptop GPU \\ Memory & 16 GB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Environment configuration.  
Figure 4: Detailed flowchart of the autonomous accident liability determination process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline Pre-trained weights & Path to the pre-trained YOLOv8 weights file & YOLOv8s.pt \\ Epochs & Number of training cycles & 15 \\ Batch size & Number of images processed per batch & 4 \\ Image size & Dimensions of the images used in training (pixels) & 640 \(\times\) 640 \\ Worker processes & Number of worker processes for data loading & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training setup for YOLOv8.  
Figure 5 illustrates the training curves for the model's precision, recall, combined precision-recall, and F1-score, each evaluated at various confidence thresholds. These metrics were evaluated across three categories: cars, pedestrians, and cyclists.  
To assess the model's performance under diverse environmental conditions, we utilized the test set of the UA-DETRAC dataset, encompassing weather conditions such as sunny, cloudy, rainy, and night. This dataset offers a robust testing environment to assess the adaptability and performance of the model across various scenarios. Figure 6 displays representative scenarios we selected from the UA-DETRAC dataset.  
The test set was segmented based on weather conditions, and the performance of the trained YOLOv8 model was assessed across each weather-specific dataset. The evaluation metrics employed included recall, precision, F1-score, and average precision (AP). Datasets  
Figure 5: Performance metrics of YOLOv8 for object detection in traffic scenarios.  
Figure 6: Representative scenarios in the UA-DETRAC dataset. A composite image demonstrating four environmental conditions—sunny, cloudy, rainy, and night—selected to highlight dataset diversity.  
were categorized into sunny, cloudy, rainy, and night conditions, containing 740, 736, 245, and 510 images, respectively. The vehicle recognition performance is summarized in Table 4.  
The results demonstrate that the YOLOv8 model consistently delivered high recognition performance across various weather conditions. Notably, the performance slightly diminished during nighttime, potentially due to the limited representation of night scenes in the KITTI dataset.