# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
## 1 Introduction  
In recent years, theories of information fusion and situational awareness have been introduced to the field of traffic safety to monitor the safety status of driving in complex road traffic environments. The Data Fusion Model maintained by the Joint Directors of Laboratories (JDL) Data Fusion Group is the most widely used method for categorizing data fusion-related functions. According to [PERSON] et al. [1], the JDL was applied to safety-assisted automotive driving. Based on the technical characteristics of vehicle-mounted multi-sensors, the JDL model was improved, including its structure and composition. Furthermore, the Pro Fusion2 model for situational awareness of driving safety has been proposed [1]. [PERSON] described the research status and development trend for methods to evaluate the traffic safety state and vehicle conflict risk prediction methods. A framework for a situational awareness model for traffic safety based on multi-source data from \"human-vehicle-road\" interactions has been proposed [2]. There are some studies on vehicle collision risk identification methods using multiple factors. Ref. [3] focused on the robustness and reliability of a future trajectory-based cooperative collision warning system. In more detail, the system enables collision decision-making by estimating and communicating vehicle positions and predicting and processing future collision trajectories. Ref. [4] proposed a new approach to real-time risk assessment for autonomous vehicles that combines real-time collision prediction studied by traffic engineers with an interaction-aware motion model. With the real-time collision prediction results of the random forest classifier serving as an example, the probability required for the Dynamic Bayesian Network (DBN) model was estimated. According to the findings, a well-calibrated collision prediction classifier can provide complementary cues to already developed interactive perception-motion models and enhance real-time risk assessment for autonomous vehicles. [PERSON] [5] screened safe trajectories by predicting the trajectories of surrounding vehicles and other traffic entities and performed collision analysis using clusters oftrajectories of interest to the main vehicle driver. However, this risk quantification only considers the forward collision risk of the primary vehicle. In real traffic scenarios, collision risks can come from all directions. There are also risk assessment methods that have been applied to urban and complex scenarios, such as those in the literature [6; 7]. Ref. [8] used a random forest classifier for risk assessment feature selection and risk prediction during the lane-change process in a highway scenario. Among the studied elements, the behavioral interaction between a vehicle during the lane-changing process and its surrounding vehicles in the target lane is crucial for risk assessment. Some researchers extracted the spatio-temporal relationship between traffic participants from the scenario as an evaluation feature, used unsupervised learning to cluster risk levels, and established risk classification models for risk assessment [9; 10]. There are relatively few studies on few-shot vehicle risk assessment in complex traffic environments. Furthermore, it is difficult to collect enough data to train models for many vehicle risk scenarios in real-world settings. Therefore, it is of great significance to precisely identify the vehicle risk posed under few-shot circumstances. Meta-learning is mostly used in learning and classification for few-shot situations. Depending on the type of meta-knowledge used, there are various ways in which meta-learning can be applied. These methods can be classified according to weights, optimizers, loss functions, metrics, attention mechanisms, hyperparameters, network topologies, and black box models. In the meta-learning process, the key part lies in the meta-learner, which undertakes two stages of meta-testing and meta-training.