## 4 Experimental Analysis
### Analysis of the Impact of Image and Text Information on Liability Determination Results  
In real-world judgment scenarios, statements from the parties involved in an accident can serve as supplementary textual information. Given the inherent subjectivity of these  
\begin{table}
\begin{tabular}{l} \hline \hline
**Traffic Accident Liability Determination** \\ \hline \hline
**I. Basic Situation:** \\ \(\bullet\) & Date and Time: 16 January 2022 \\ \(\bullet\) & Location: [Specific Location] \\ \(\bullet\) & Vehicles of the Parties: \\ \(\bullet\) & **White car:** To avoid an elderly person who suddenly crossed the road, emergency braking was performed. \\ \(\bullet\) & **Black car:** Driving at a high speed, failed to stop in time, and collided with the white car. \\ \(\bullet\) & **Truck:** Avoided direct collision with the white car, but just rotated. \\ \(\bullet\) & Road and traffic environment: No traffic signals or traffic signs control. \\ \(\bullet\) & How the accident happened: \\ \(\bullet\) & On a highway without traffic lights, an elderly person suddenly crossed the road. To avoid the elderly person, the driver of the white car braked urgently, and the black car behind failed to brake in time due to excessive speed, and collided with the white car. At the same time, a truck was also present, but it only rotated and did not collide with other vehicles. \\ \(\blacksquare\) & **II. Accident Evidence and Analysis:** \\ \(\bullet\) & Accident evidence: Key frames of surveillance video, witness testimony. \\ \(\bullet\) & Analysis: After analysis, the elderly person’s crossing was the direct cause of the accident, and the excessive speed of the black car and the failure to maintain a safe distance were the main causes of the collision. \\ \(\blacksquare\) & **III. Fault and responsibility analysis:** \\ \(\bullet\) & **White car:** Emergency brake to avoid pedestrians, no fault. \\ \(\bullet\) & **Black car:** Failed to maintain a safe distance, failed to slow down in time according to road conditions, bear full responsibility. \\ \(\bullet\) & **Truck:** No collision, no responsibility. \\ \(\bullet\) & **Responsibility determination:** \\ \(\bullet\) & According to Article 43 of the Road Traffic Safety Act of the Republic of China, the driver of the black car violated the regulation that they must maintain a necessary safe distance from the vehicle in front, and therefore should bear full responsibility. The responsibility ratio is as follows: \\ \(\blacksquare\) & **Black car:** Bears full responsibility, the responsibility ratio is 100\%. \\ \(\blacksquare\) & **White car:** No fault, the responsibility ratio is 0\%. \\ \(\blacksquare\) & **Truck:** No fault, the responsibility ratio is 0\%. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Liability determination letter generated by the model.  
statements, they often carry strong biases that may influence the model's judgment. When the extracted key frames do not clearly depict the accident scene, this supplementary textual information becomes critical for determining judgment outcomes. To examine this effect, we conducted two experiments.  
**Experiment 1:** We aimed to explore the influence of subjective statements from the parties involved in the accident on the model's judgment. The key frames extracted in Figure 13 were kept unchanged, and various textual descriptions reflecting the parties' subjectivity were introduced. The model was tasked with evaluating whether these different statements could guide the judgment outcomes and whether the text accurately matched the key frames. Additionally, the model was required to determine responsibility based on a combined analysis of the key frames and accompanying textual descriptions.  
**Experiment 2:** We removed a key frame depicting a vehicle crash and obscured vehicle information to assess the impact on the accuracy of the generated liability determination.