Search Results

At present, most of the game decision lane-changing models only consider the state data of the vehicle at the current moment. However, the driving style has a significant impact on the vehicle trajectories, which should be taken into account in the lane-changing process. Moreover, most of the game models are static and do not take into account the sequence of the vehicle lane-changing. This paper proposed a Stackelberg-game-based vehicle lane-changing model considering driving style. Firstly, the NGSIM public dataset is selected for this research and the data screen flow is processed. The K-means algorithm is applied to exchange data clustering. Based on the analysis of vehicle lane changing features under different driving style, the characteristics of the corresponding data under different style are extracted. The quantic-polynomial programming algorithm is used to generate a vehicle lane changing trajectory under different driving styles.

To reduce the fuel consumption of an intelligent four-wheel-drive (4WD) electric vehicle (EV), this paper presents a new method of speed trajectory planning. The proposed method can realize a fast real-time optimization of vehicle speed, aiming to achieve the minimum motor energy output according to the fuel consumption directly. In addition, the optimization method maintains the cruising speed within the deviation required to achieve a good control effect. First, the road slope information is considered, and then, a 4WD EV longitudinal dynamic prediction model and a fuel consumption function are established. Next, the state and control variables are chosen to establish the cost function; in this manner, the MPC optimization problem in each prediction horizon is transformed into quadratic form. Finally, the fast solving tool called GRAMPC is used to solve the MPC problem.