Abstract: Understanding and identification of driver's driving style are of great significance to the human-machine harmonious interaction under different control systems such as automatic driving and assistant driving. A driving style identification strategy based on random forest model is proposed in this paper. Firstly, the driver's driving data acquisition system is set up. Based on that, the driving data of several drivers are collected in real time under typical car-following scenarios. According to hierarchical clustering theory, the driving style are “labeled”. On this basis, a random forest model is introduced to establish driving style identification strategy, and importance analysis, model training and identification test are carried out. The test results show that the driving style identification strategy based on the random forest model can effectively identify driver's driving style and the overall accuracy of the model can reach 97.1%

Key words: vehicle engineering, driving style identification, random forest, hierarchical clustering, car-following condition