Zhu, B., Li, W., Bian, N., Zhao, J. et al., "Identification of Driver Individualities Using Random Forest Model," SAE Technical Paper 2017-01-1981, 2017.
Driver individualities is crucial for the development of the Advanced Driver Assistant System (ADAS). Due to the mechanism that specific driving operation action of individual driver under typical conditions is convergent and differentiated, a novel driver individualities recognition method is constructed in this paper using random forest model. A driver behavior data acquisition system was built using dSPACE real-time simulation platform. Based on that, the driving data of the tested drivers were collected in real time. Then, we extracted main driving data by principal component analysis method. The fuzzy clustering analysis was carried out on the main driving data, and the fuzzy matrix was constructed according to the intrinsic attribute of the driving data. The drivers’ driving data were divided into multiple clusters. The random forest model was trained based on the driving data that has been "labeled", and the individual characteristic of the driver could be identified by the trained random forest model. At the same time, the traditional identification algorithms were also used to identify the driver individualities, and the identification results of several algorithms were compared. The results showed that the proposed method based on random forest has the higher accuracy and could effectively identify the driver individualities.