Research on the Classification and Identification of Driver's Driving Style

More and more Advanced Driver Assistance Systems (ADAS) are entering the market for improving both driving safety and comfort. To improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers, it is important to understand human drivers' driving habits that make the systems more human-like or personalized for ADAS. The research presented in this paper proposes a method to classify and recognize drivers' driving styles. A typical testing scenarios is created under a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with both PanoSim-RT and dSPACE. The driving styles are defined and classified into three categories based on the root mean square of vehicle acceleration, the Time-to-Start and the Time-To-Follow. Samples with 21 drivers are used for the testing with driving data collected, analyzed and further employed for driving style recognition via Multi-dimension Gaussian Hidden Markov Process (MGHMP). Test results show that driving styles can be classified clearly and identified effectively using the proposed classification and identification strategy.