基于宏微观因素的概率级别的车辆事故预测

车辆事故预测是避免道路车辆事故发生的重要研究课题.以往的研究使用的事故数据集只包含地理情况、环境情况、交通情况等宏观因素，或者只包含车辆行为和驾驶员行为等微观因素.因为很难收集到同时包含2类因素的事故数据集，很少有研究将这2类因素结合起来，然而车辆事故往往是两者共同作用的结果.此外，在收集到的数据中没有可以用于预测的事故发生概率标签，所以目前多数的研究关注点只是在于事故是否发生而不能得到准确的概率值.然而在实际应用场景下，驾驶员需要的是不同级别的危险预警信号，而这种信号正是应该由事故概率值决定的.2019年发布的事故宏观因素数据集OSU(Ohio State University)与宏观因素数据集FARS(fatality analysis reporting system)和微观因素数据集SHRP2(strategic highway research program 2)都具有一些相同的特征，为它们的融合提供了机遇.因此,首先得到了一个同时包含宏观和微观因素的数据集，其中事故数据(正样本)融合自OSU、FARS数据集，以及与SHRP2分布相同的数据集Sim-SHRP2(simulated strategic highway research program 2)，而安全驾驶数据(负样本)则由自己驾驶汽车获得.然后，针对收集到的数据中没有概率标签的问题，还设计了一个概率级别的无监督深度学习框架来预测准确的概率值，该框架使用迭代的方式为数据集生成准确的概率标签，并使用这些概率标签来进行训练.实验结果表明，该框架可以使用所得到的数据集来灵敏而准确地预测车辆事故.

Car Accident Prediction Based on Macro and Micro Factors in Probability Level

Car accident prediction is an important problem to study for avoiding the accidents. Previous studies make the prediction for a car based either on macro factors such as geography, environment and traffic or on micro factors such as car and driver behaviors. There is rarely a study combining the two types of factors because it is difficult to collect the two types of data at the same time. However, car accidents usually result from both of the two types of factors. In addition, the current researches predict whether an accident will happen or not. There is rarely a study providing a more accurate accident probability because there is no probability label for use in the collected data. However, such a probability is useful to notify the driver in different warning levels. The OSU(Ohio State University) accident dataset of macro factors published in 2019 has some identical characteristics with the FARS(fatality analysis reporting system) dataset of macro factors and SHRP2(strategic highway research program 2) dataset of micro factors, and thus provides an opportunity to fuse them. Therefore in this paper, we obtain a dataset of both macro and micro factors. In the dataset, accident data (positive data) is fused from the OSU and FARS datasets, as well as Sim-SHRP2(simulated strategic highway research program 2) similar to the SHRP2 dataset, while safe-driving data (negative data) is obtained by ourselves driving a car. In addition, since the obtained dataset does not have any probability label, we also design a probability-level unsupervised deep learning framework to predict the accurate probability. The framework iteratively generates accurate probabilities from the obtained dataset, and is trained with the generated probabilities. The experimental results indicate our framework can predict car accidents with the obtained dataset sensitively and accurately.