Abstract:To realize the accurate identification of vehicle abnormal behavior and improve the safety of vehicle driving, an abnormal driving behavior recognition model based on SVM supervised learning and LSTM deep learning distribution was proposed. Firstly, the vehicle trajectory data were filtered to construct the abnormal behavior data set. Secondly, based on the trajectory characteristics of abnormal behaviors, the feature labels of specific abnormal behaviors were calibrated in the training set extracted and artificially. Thirdly, several SVM models were constructed to roughly identify the training set, and abnormal behaviors were screened from the test set based on the dichotomy principle of SVM. Finally, the LSTM model was used to construct specific types of abnormal behavior models, and through the method of deep learning, the abnormal behavior data were subdivided into specific abnormal driving behaviors such as weaving, swerving, sideslippling, turing with a wide radius, fast U-turn, and sudden-braking. The trajectory data of US-101 highway and peachtree urban road in NGSIM was applied to verify the performance of SVM and LSTM double recognition models, using root mean square error and recognition accuracy, etc. The results show that the constructed double-layer recognition model has 98 % recognition accuracy in the first layer and more than 80 % recognition accuracy in the second layer, this model can accurately identify abnormal driving behaviors in big data and determine their types.