# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
## 4 A Prototype Network-Based Risk Level Identification Model for Lane Change
### Experimental and Results
In order to achieve the classification and evaluation of the vehicle risk level under few-shot classification, this paper first maps the vehicle risk feature signals to the risk field feature metric space through a prototype network, learns the prototype measurement of each vehicle risk state, and evaluates the risk posture through the prototype network. The experimental results indicate that the highest classification accuracy is obtained in 6-way 5-shot for \(\alpha=0.00001,iters=1000\), in 6-way 10-shot for \(\alpha=0.00001,iters=100\), and in 6-way 20-shot for \(\alpha=0.001,iters=100\). Based on sharing the vehicle risk threshold space with the vehicle risk field, based on features extracted from self-driving content and related contents of surrounding vehicles, univariate or multivariate characteristic risk posture evaluation indexes were improved, and the representativeness and discriminability of risk-level discrimination features were enhanced. The research field of few-shot recognition mainly focuses on image processing, but it seldom deals with the processing of vehicle driving data. This paper provides theoretical ideas for the processing of few-shot data for vehicles, especially the identification of risk data. The latest results can improve the efficiency of few-shot lane-change risk identification, which provides practical guidance for risk identification and decision analysis for future safe lane-change trigger scenarios based on expected functional safety, as well as certain theoretical support for the establishment of L3+ autonomous vehicles. In the current industry, research combining lane-change risk with small sample models has not yet emerged, which gives this study unprecedented uniqueness and innovativeness. Typically, researchers analyze lane-change risk using large sample data or simulation-based data, but these approaches often fail to fully capture the individualized characteristics and variability present in actual driving situations. Therefore, the integration of lane-change risk with small sample models offers researchers a fresh perspective, aiding in a more precise understanding of risk factors and decision-making processes in driving behavior. Despite the lack of previous research for comparison, this also signifies an opportunity for exploration in uncharted territories. By leveraging small sample models, researchers can delve deeper into the behavioral characteristics of individual drivers and propose more targeted strategies for managing driving risks. The widespread application of this research methodology could have profound implications for the field of traffic safety, providing more accurate and reliable tools and methods for risk assessment and prediction in real driving scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Loss Type**} & \multicolumn{3}{c}{**Test Accuracy**} \\ \cline{2-4}  & **6-Way 5-Shot** & **6-Way 10-Shot** & **6-Way 20-Shot** \\ \hline \(\alpha=0.01,iters=100\) & 0.6879 & 0.8539 & 0.7876 \\ \hline \(\alpha=0.05,iters=100\) & 0.6292 & 0.5903 & 0.7268 \\ \hline \(\alpha=0.1,iters=100\) & 0.7354 & 0.9173 & 0.7031 \\ \hline \(\alpha=0.001,iters=100\) & 0.6899 & 0.7011 & 0.8398 \\ \hline \(\alpha=0.00001,iters=100\) & 0.7913 & 0.9149 & 0.8488 \\ \hline \(\alpha=0.00001,iters=1000\) & 0.9068 & 0.7441 & 0.8520 \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental results.