# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
## 5 Conclusions and Future Work  
The broad implication of this study is to solve the problem of the effective identification of vehicle risk and the monitoring of road vehicle safety under the condition of few-shot vehicle risk data in a complex traffic environment. On this basis, a vehicle lane-change risk situation identification model based on the combination of a prototype network and the risk field under few-shot classification was proposed for the identification of a vehicle lane-change risk situation under a few-shot approach. Through the prototype network, the vehicle risk characteristic signal is mapped to the risk field characteristic measurement space. The measurement prototype of each risk state of a vehicle is learned. Through the assessment of the risk status under the prototype network, the classification and evaluation of the vehicle risk level under few-shot classification can be realized. More research has focused on the role of image processing in a few-shot signaling pathway. According to some recent findings, a few-shot approach might be involved in another important pathway. In addition, this study has provided ideas for the processing of vehicle few-shot data, especially risk identification. First of all, the features extracted from the subject vehicle content and the related content of surrounding vehicles were extracted, and the vehicle risk threshold space was shared with the vehicle risk field. In this way, univariate or multivariate risk posture evaluation indexes were enhanced, and the representativeness and discriminability of risk posture discriminant characteristics were refined. The lane-change data were extracted from a publicly available data set, and they include nine different variables: \(v\_vel,v\_Acc,Space\_Hduy,Time\_Hduy,\triangle x,\triangle y,\triangle a,\)\(TTC,\) and \(1/TTC\). According to [PERSON]'s correlation analysis, the characteristic parameters of the driving risk field assessment were obtained. In addition, five characteristic parameters were selected as input variables in the risk-level assessment model. Then, a risk classification mechanism combining the vehicle risk field and a prototype network was proposed, which obtained more important attribute features as the input data of the prototype network by matching the attributes of different risk fields, and a large number of experiments were conducted based on few-shot data. The risk field strength was computed using the risk field model. Based on various distributions of left and right channel changes, risk thresholds were generated. Thus, the weight of each variable was determined using the entropy approach. The prototype network was used to learn the prototype metric for each risk condition of a vehicle. Then, the vehicle risk feature signal was mapped to the risk field feature metric space. The prototype network evaluated the risk posed and completed the assessment and classification of vehicle risks for a few-shot approach. Inevitably, there were drawbacks. In this paper, the following responsibilities for a lane were divided into following responsibilities that did not belong to the responsibility of the car driver. As the following car in this lane was not included in the analysis, only the data for the cars in front of the lane, the front, and the rear cars in the target lane were examined. In addition, this paper did not take into account more types of driving scenarios. Meanwhile, this paper only studied the risk scenario of lane changes in public data sets. This inquiry gathered data sets from the expected functional safety field and applied the few-shot algorithm to path following, which may cause related problems in the expected functional safety field. The above problems need further study. Conceptualization, D.W.; methodology, D.W.; software, D.W.; validation, D.W. and C.Z. ; formal analysis, D.W. and C.Z. ; investigation, D.W. and C.Z. ; resources, Y.L. ; data curation, D.W.; writing--original draft preparation, D.W.; writing--review and editing, C.Z. and Y.L.