## 5 Discussion
### Limitations of the Algorithm  
The performance of the improved HS algorithm relies heavily on the threshold \(T\), which regulates the adjustment of the smoothing parameter. Although effective in controlled experiments, its robustness under varied and unpredictable traffic conditions remains insufficiently validated. In extreme lighting conditions or with sudden changes in scene dynamics, the preset threshold may result in suboptimal traffic estimates.  
The dataset sourced online for training the SVM for collision detection may not fully capture the diversity of real-world driving scenarios, particularly under adverse weather conditions such as heavy rain or fog. This limitation could impede the model's ability to generalize effectively across various scenarios.  
The application of large language models like GPT-4 in legal contexts, such as automated accident liability determination, raises concerns because of their susceptibility to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Changes in** & **Input Explanation** & **Responsibility Determination Result** \\ \hline \multirow{6}{*}{Figure 14} & \multirow{6}{*}{\begin{tabular}{c} The frame at the moment of collision \\ was removed from the eight key frames. \\ \end{tabular} } & Fault and liability of the parties causing the road traffic accident or accidental cause \\  & & Elderly woman: According to Article 62 of the Road Traffic Safety Law of the People’s Republic of China, “Pedestrians shall use crosswalks or pedestrian crossing facilities \\  & & when passing through intersections or crossing roads”. The elderly woman did not comply with this provision, which directly led to the white car taking emergency \\  & & measures. Therefore, she bears primary responsibility for the accident. \\  & & Driver of the white car: His behavior was an emergency avoidance, but he did not consider the risks that emergency braking might bring to the rear vehicle, so he bears secondary responsibility. \\  & & Driver of the black car: He failed to maintain a safe distance, which violated Article 43 of the Road Traffic Safety Law of the People’s Republic of China, “For motor vehicles traveling in the same lane, the rear vehicle shall maintain a safe distance from the front vehicle sufficient to take emergency braking measures”. Therefore, he bears secondary responsibility for the accident. \\ \hline \multirow{6}{*}{Figure 15} & \multirow{6}{*}{\
\begin{tabular}{c} Key information at the moment of collision is \\ obscured \\ \end{tabular} } & \multirow{6}{*}{
\begin{tabular}{c} How the accident happened: \\ [PERSON] was driving a white car along the main road. Because an old man suddenly crossed \\ the road in front of him, he stopped suddenly to avoid him. [PERSON] was driving a black car \\ about 20 m behind. Due to the high speed, he failed to react in time and rear-ended the white car. \\ \end{tabular} } \\  & & Responsibility determination: \\  & & According to the “Road Traffic Safety Law of the People’s Republic of China”: \\  & & [PERSON]’s behavior was a normal risk avoidance behavior and he was not at fault. \\  & & [PERSON] failed to maintain a safe distance and drove at a high speed, which violated Article 43 of the “Road Traffic Safety Law” and should bear the main responsibility. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model performance with incomplete image information.  
generating plausible yet factually incorrect outputs [32; 33; 34]. This risk is especially significant in legal contexts where precision is paramount.