Abnormal driving behaviours, such as rapid acceleration, emergency braking, and rapid lane changing, bring great uncertainty to traffic, and can easily lead to traffic accidents. The accurate identification of abnormal driving behaviour helps to judge the driver's driving style, inform surrounding vehicles, and ensure the road traffic safety. Most of the existing studies use clustering and shallow learning, it is difficult to accurately identify the types of abnormal driving behaviours. Aimed at addressing the difficulty of identifying driving behaviour, this study proposed a recognition model based on a long short-term memory network and convolutional neural network (LSTM-CNN). The extreme acceleration and deceleration points are detected through the statistical analysis of real vehicle driving data, and the driving behaviour recognition data set is established. By using the data set to train the model, the LSTM-CNN can achieve a better result.

中文翻译：

---

长短期记忆和卷积神经网络，用于异常驾驶行为识别

快速加速，紧急制动和快速变道等异常驾驶行为给交通带来极大的不确定性，并容易导致交通事故。准确识别异常驾驶行为有助于判断驾驶员的驾驶风格，告知周围的车辆并确保道路交通安全。现有的大多数研究都使用聚类和浅层学习，因此很难准确地识别异常驾驶行为的类型。为了解决识别驾驶行为的困难，本研究提出了一种基于长短期记忆网络和卷积神经网络（LSTM-CNN）的识别模型。通过对真实车辆行驶数据的统计分析来检测极端加减速点，并建立驾驶行为识别数据集。通过使用数据集训练模型，LSTM-CNN可以获得更好的结果。