Identification of Driver Individualities using Random Forest Model

Paper #:
  • 2017-01-1981

Published:
  • 2017-09-23
Abstract:
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, the driver individualities recognition method is constructed in this paper under typical conditions by 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 driver were collected in real time under typical driving conditions. Then, we extract main driving data by principal component analysis method. The fuzzy processing is carried out on the main driving data, and the fuzzy matrix is constructed according to the intrinsic attribute of the driving data. The clustering relationship is determined by certain membership degree, and the driver's driving data is divided into multiple clusters. The random forest model is trained based on the driving data that has been standardized, and the individual characteristic of the driver could be identified by the trained random forest model. At the same time, the traditional identification algorithms are also used to identify the driver individualities, and the parameters of several algorithms are compared. The results show that the proposed method based on random forest has the highest accuracy and could effectively identify the driver individualities.
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