Abstract: In order to integrate different driving styles into the vehicle test system and make the results more close to the reality, a novel method for driver modeling based on real-world road test data and neural network is proposed. Considering the divergence and local mutability of the real-world data, the cerebellar model articulation controller(CMAC), a locally designed neural network model, is utilized together with the direct inverse model approach to accomplish this model, which is used to replace the real driver or PID-like model for laboratory federal test procedure(FTP) test. On the other hand, a vehicle test data(VTD) and neural network based vehicle model is established and employed for simulation test. VTD and FTP based test are conducted to verify the effectiveness of the proposed scheme. Meanwhile, the personalized driver model is able to not only complete the standard vehicle test, but also mitigate the fatigue during driving.

Key words: cerebellar model articulation controller, driver model, federal test procedure(FTP), vehicle test data