关键词: 驾驶风格识别；强化学习；混合示教型神经网络；多任务学习；聚类分析

Abstract: In order to improve the accuracy of vehicle driving style recognition, the article builds a driving style recognition method based on long and short-term memory neural network combined with hybrid schematic neural network. The article dissects the relationship between drivers' driving styles and time series data by mining the time series features in driving behavior data, using the public dataset NGSIM, extracting descriptive features after screening and smoothing of the dataset, converting the high-dimensional features to low-dimensional ones by principal component analysis, and specifying the number of clusters by K-means method to be identified as three driving styles. Then the classification recognition of driving styles is carried out by multi-task learning, multi-task fusion, long short-term memory (MTL-MTF-LSTM) neural network, which combines reinforcement learning and imitation learning, after multi-task assignment of recognition pool. The results show that the model achieves 95%, 98% and 97% recognition accuracy for conservative, general and aggressive driving styles, respectively, with an overall excellent performance.

Key words: driving style recognition; reinforcement learning; hybrid teaching neural network; multi-task learning; cluster analysis