## 1 Introduction  
As global urbanization accelerates, car ownership has surged and urban traffic density has increased significantly [1; 2; 3], posing significant challenges to traffic management. This situation not only increases the frequency of minor traffic accidents, but also exacerbates traffic congestion. Although minor accidents typically do not result in casualties, the efficient and accurate management of these incidents is crucial for public safety and the smooth operation of transportation systems [4].  
Recent advancements in machine learning and artificial intelligence have paved the way for new methods to predict and prevent traffic accidents. For instance, [PERSON] et al. [5] utilized machine learning techniques to predict the severity of road accidents, demonstrating the potential of computational tools in enhancing traffic safety proactively. [PERSON] et al. [6] developed an integrated model that combined convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism, which significantly enhanced highway traffic flow prediction and demonstrated the potential of advanced predictive models in improving traffic safety. Similarly, [PERSON] et al. [7] proposed a dynamic spatial-temporal attention (DSTA) network that utilized dash-cam video data to predict traffic accidents early by learning the spatial-temporal features, enhancing accident prevention preparedness.  
However, traffic accidents continue to occur despite these advancements. When a minor traffic accident occurs, a primary cause of traffic congestion is the challenge in determining the liability of the involved parties. This process typically involves waiting for traffic police to arrive at the scene to formally assign liability [8]. During this time, the affected lanes remain blocked, exacerbating traffic delays and congestion. Consequently, there is a pressing need for more advanced technical solutions to address traffic accidents.  
Currently, the majority of methods for managing minor accidents still depend heavily on manual review. One approach is to manage liability determination online to minimize manual intervention. For instance, the \"Traffic Management 12123\" mobile app, promoted by Qingdao City, enables individuals involved in minor road traffic accidents to resolve disputes independently through online regulations [9]. Although this initiative has been somewhat successful, with the capability for online liability determination, self-resolution rates remain below 20%, and the overall effectiveness of the process is still unsatisfactory.  
Some scholars have proposed automatic accident liability determination schemes based on traffic accident collision detection [10; 11; 12; 13], however, the results of liability determination are not clear enough, and the specific proportion of accident liability cannot be accurately divided. Moreover, the scope of such liability determinations is quite limited and fails to address the complexities of dynamic traffic environments.  
In summary, there is an urgent need to explore more efficient and precise technical methods to enhance the handling of accidents and alleviate both traffic congestion and the burden on traffic police.  
Based on this foundation, our study introduced an autonomous method for determining liability in minor traffic accidents, leveraging collision detection and large language models. This method was designed to autonomously manage the rapid resolution of traffic incidents by precisely quantifying the proportion of responsibility for each party without human intervention. This capability aims to streamline traffic management, potentially reduce the need for on-site police intervention, and enable traffic authorities to allocate resources more effectively.