Anytime, anywhere, anyone can collect not only personal information but also surrounding situation information from home or a vehicle on the move without being restricted by time and space. To this end, the need for various devices such as biosensors, wearable devices, and temperature sensors is increasing. In this circumstance, there are many problems with the interaction of multiple devices and data processes, and there is a lack of structured or unstructured emerging contextual data. Accordingly, it is necessary to come up with a data collection method to obtain a variety of information efficiently. Such a method can obtain a diversity of information consistently with the use of data convergence. Since the method makes possible decision making, entity identification, and context prediction, it is applicable to diverse areas, including object detection and context awareness. The data generated through convergence makes it possible to the improve performance of data analysis through machine learning and deep learning. For this reason, a variety of modal types are applied to data analysis. Modal convergence for analysis can improve analysis performance more than unimodal. In this study, propose the accident risk prediction model based on attention-mechanism LSTM using modality convergence in multi-modal. In order to predict the risk of an accident, the proposed method makes the convergence of structured and unstructured modality data on the basis of judgmental fusion and statistical fusion and generates data sets. It is capable of solving the problem of data shortage and obtaining a variety of information consistently. In the proposed method, firstly, preprocessing is performed, and data sets of accident risk information are generated with the uses of the road risk equation and accident risk equation. Next, the correlation coefficient and regression coefficient for each variable is calculated through regression analysis modal and correlation coefficient analysis modal. A correlation coefficient is used to judge which variable needs to be learned intensively in order for the attention-mechanism of the accident risk prediction model. A regression coefficient is used as attention-weight to adjust a data weighting factor in attention-mechanism LSTM. Accordingly, whether there is the risk of an accident is judged. Performance evaluation is conducted in two ways. Firstly, epoch-based loss is evaluated in the comparison between the attention-mechanism LSTM-based accident risk prediction model with convergence modal and the model without it. In short, their RMSE is evaluated and compared. Secondly, the proposed model is compared with conventional models in terms of RMSE.

无论何时何地，任何人都可以从家中或行驶中的车辆中收集个人信息，还可以收集周围情况信息，而不受时间和空间的限制。为此，对诸如生物传感器，可穿戴设备和温度传感器之类的各种设备的需求正在增加。在这种情况下，多个设备和数据过程之间的交互存在许多问题，并且缺少结构化或非结构化的新兴上下文数据。因此，有必要提出一种数据收集方法以有效地获得各种信息。通过使用数据收敛，这种方法可以获得一致的信息多样性。由于该方法使决策，实体识别和上下文预测成为可能，因此适用于各个领域，包括对象检测和上下文感知。通过收敛生成的数据可以通过机器学习和深度学习提高数据分析的性能。因此，将多种模式类型应用于数据分析。用于分析的模态收敛比单模态更能提高分析性能。在这项研究中，提出了一种基于注意机制LSTM的多模态模式收敛的事故风险预测模型。为了预测事故的风险，该方法在判断融合和统计融合的基础上，使结构化和非结构化模态数据收敛，并生成数据集。它能够解决数据短缺的问题，并始终如一地获取各种信息。在提出的方法中，首先，进行预处理，并使用道路风险方程式和事故风险方程式生成事故风险信息数据集。接下来，通过回归分析模态和相关系数分析模态来计算每个变量的相关系数和回归系数。相关系数用于判断需要集中学习哪个变量，以提高事故风险预测模型的注意力机制。回归系数用作关注权重，以调整关注机制LSTM中的数据加权因子。因此，判断是否有发生事故的危险。绩效评估有两种方式。首先，通过比较基于注意机制的基于LSTM的具有收敛模式的事故风险预测模型与不具有该模型的模型，可以评估基于时间段的损失。简而言之，对他们的RMSE进行了评估和比较。其次，就RMSE而言，将所提出的模型与常规模型进行了比较。