# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
## 1 Introduction
In recent years, metric-based meta-learning, optimization-based meta-learning, and model-based meta-learning have emerged as the key methodological models for constructing meta-learners. Few-shot meta-learning is frequently employed in applications including classification [11; 12], target detection [13; 14], face recognition [15], medical visualization [16], and video synthesis [17]. Depending on the annotated labels in these cases, the majority of samples in other categories may be inherently rare or difficult to obtain. For real-world events, the distributions of the tested data and the training set are not always constant, and sometimes, they are even more complex than the experimental setting. The actual collected data to be tested are changeable in nature. In [16], [PERSON] et al. combined unsupervised denoising autoencoders with supervised meta-learning to overcome the data limitations of medical vision questions and answers. By using a few-shot set to train the proposed model, it was found that the model performed better than any existing medical vision question-and-answer methods. [PERSON] et al. [17] proposed a meta-learning face recognition method with optimal performance in cross-ethnic and cross-scene tests that can significantly improve the generalization performance of the model. Previous studies mainly focused on the role of image processing in the few-shot signaling pathway. According to some recent findings, the few-shot approach might be involved in another important pathway. Based on the knowledge prototype networks of radar working patterns, [PERSON] et al. [18] carried out multifunctional few-shot radar working pattern recognition and compared its performance with that of traditional machine learning algorithms. Prototypical networks are a metric-based few-shot learning method that aim to learn the prototype representation of each type of instance in a metric space. [PERSON] et al. [19] analyzed fault diagnosis systems using prototype networks. Through the measurement of each type of prototype with query instances, query instances are classified into the closest category. For categorization, the distance between the query instances and the prototypes is compared, and the category closest to them is identified. As for the prototype network, there is no need to augment the dataset with random noise or unlabeled samples. Therefore, there is no model collapse, a problem for generator models and deep semi-supervised networks that exists in few-shot fault diagnosis methods for generative adversarial networks and deep semi-supervised networks. In light of the above ideas, the prototype network was applied to the field of vehicle risk identification, and data training and testing were carried out on the basis of identifying the strength of the vehicle risk field so as to realize the identification of the risk posed by vehicle lane changes. In summary, the main contributions are as follows:Utilizing features extracted from both the self-vehicle content and the surrounding vehicle-related content, we aimed to refine the representation and discriminability of risk discrimination features by sharing the vehicle risk threshold space with the vehicle risk field. This enhancement was achieved through univariate or multivariate combinations, enhancing the clarity of the risk assessment index. * We propose a risk classification mechanism that integrates the vehicle risk field and prototype network. This mechanism aims to match attributes from various risk fields to identify more crucial attribute features as input data for the prototype network. Extensive experiments conducted with few-shot data demonstrate that our proposed algorithm effectively enhances the accuracy of vehicle risk identification. * We employed a prototype network to predict vehicle risks and introduce a novel vehicle risk assessment method with few-shot capabilities.