The road feature has an important influence on the safe speed of the unmanned vehicle and the safe space between two vehicles. Real-time access to the features of the road ahead of time and timely adjustment of engine torque are significant to unmanned driving. Most of the researches nowadays make full use of vehicle sensor technology and environment perception technology. Vehicle sensor is widely used to collect the features of the road. While in this paper, a new type of road feature extraction is proposed based on vehicle speed change. Under the premise of less sensor installed, vehicle speed-time data series is collected. The pavement parameters can be estimated with vehicle speed. Based on the vehicle dynamics, this paper studies the relationship between vehicle speed and rolling resistance. Different road features have different influences on road friction resistance. According to the change of vehicle dynamics parameters on the same road under different driving conditions, the pavement characteristic parameter identification model is established, and the pavement characteristic parameters are estimated. The model is validated by the hardware-in-the-loop simulation platform. Finally, real world data was collected with testing to validate the appropriateness of the proposed model. The experimental results show that the error of the road rolling resistance is less than 3% when the speed accuracy can reach 0.01 km/h and the sampling frequency is over 10 Hz. The method of determining the pavement characteristic parameter is feasible. This article, which couples vehicles and roads, can use less vehicle sensors, providing a new way of thinking for unmanned research.