Abstract: Shift schedule is one of the major factors for drivability. When using traditional method to establish shift schedule, it considers power performance and fuel economy, but neglects driver characteristics. Speed and throttle in traditional two-parameter shift schedule may reflect vehicle performance for driver to some extent, but driving characteristics of different drivers can't be considered. In this paper, a shift schedule method based on driver type was proposed for making vehicle maneuverability meet drivers' characteristics. In order to obtain the drive type, driving behavior and intention were analyzed according to drivers' operations in driving process, different driver characteristics were obtained, and then drivers could be classified into conservative and sport type. So identification scheme of driver type was proposed. Driver's operations, road condition and vehicle state were transformed into electrical signals by vehicle sensors. These electrical signals could be identified by electronic control unit and used to classify driving style, and then driver type could be obtained by fusion decision of driving style. Firstly, BP (back propagation) neural network classifier was employed for driving style identification from the obtained signals. The classifier designed had 3 layers, and any 2 layers were linked by nonlinear S-functions. The data of effective driving cycles and corresponding characteristic signals, which could remarkably characterize the driving style, were placed in the input layer, the different driving styles were obtained from the output layer, and the node number of the middle layer was optimized by the empirical formula. Moreover, the classifier was trained off-line on the basis of the driving data under various working conditions. Secondly, the driving styles were fused by Bayesian to obtain driver type. Because there were many different effective driving cycles while driving, the fusion decision process was performed in 2 stages. The fusion decision of driving style date of the same effective driving cycle was accomplished at the first stage, and the driver type was obtained at a later stage which was fusion decision of different effective driving cycles. Finally, the conception of power performance coefficient was proposed in this paper. It could be calculated with practical throttle opening, small opening threshold and large opening threshold. The values of small and large opening threshold were determined by driver type. By a change of power performance coefficient corresponding to driver type, the proportion of power performance and fuel economy in shift schedule could be adjusted. After the strategy of the power performance coefficient was determined, a comprehensive shift schedule based on driver type, power performance and fuel economy was presented. The more conservative the driver type was identified, the more attention the fuel economy was paid in the comprehensive shift schedule based on driver type. Conversely, the more sport driver type corresponded to the more power factor. In order to validate the reliability of comprehensive shift schedule based on driver type, vehicle shifting processes corresponding to different driver types were simulated by the test vehicle equipped with six-speed dual clutch transmission. This simulation was performed under the condition of starting with 50% throttle opening, and vehicle speed and gear position were observed. The simulation results showed that the more sport driver type corresponded to a higher speed of shift points under the condition of same throttle opening, which led to later upshift and higher vehicle speed at the same time, and then sport driver could obtain better power performance. So the shift schedule based on driver type proposed in the paper is feasible and efficient, and can meet the requirements of different drivers. The research provides a reference for driver style identification and intelligent shift schedule establishment.