Accurate tracking of a given path is one of the primary factors in the maneuverability of a vehicle and is also an important topic in autonomous vehicle research. To solve the problem of vehicle path tracking, the problem must first be transformed into an optimal control problem. Then, a symplectic pseudospectral method (SPM) based on the third-generation function of symplectic theory and pseudospectral discretization is proposed to efficiently solve the nonlinear optimal control problems. Finally, the results obtained by the proposed algorithm are compared with those obtained by the Gauss pseudospectral method (GPM). The simulation results show that the proposed method can effectively solve the vehicle path tracking problem. Furthermore, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and greater applicability than other methods.

This paper proposes a new optimal control method to solve traffic accidents such as vehicle collisions during driver’s conscious lane changing under the dangerous state of highway driving and meet the requirements of safety and comfort during vehicle lane changes. First, by directly analyzing the interpolation error and slope of discrete nodes, a new mesh refinement method was used to solve the vehicle optimal lane-changing problem. Then, a simulation of vehicle emergency left and right lane changes was established to verify the proposed method based on a nonlinear 7-DOF vehicle model. Finally, a virtual test using the CarSim software was conducted to verify the feasibility of the simulated results. The simulation results show that the mesh generated by the method in this study is smaller and requires fewer grid iterations, which can quickly and accurately solve the vehicle optimal lane-changing problem with constant components of the optimal control variables and fewer mesh iteration times and node numbers with high accuracy. And also, compared with the traditional Gaussian pseudospectral method(GPM), the solution accuracy of the mesh refinement method is higher than that of the traditional Gaussian pseudospectral method, indicating the superiority of the proposed method.

Obtaining vehicle status in real-time and accurately during the driving process is of great significance for active safety control of the vehicle. In response to this problem, combining a 7-DOF vehicle dynamic model and the magic formula tire model, the research designed a time-sensitive and robust double cubature Kalman filter (DCKF) observation algorithm. The DCKF algorithm addressed singular value decomposition to optimize the error covariance matrix, and connected driving state observer information of the vehicle to update the observation signal realizing the real-time estimation of the vehicle state. The DCKF algorithm is verified on the simulation platform, and compared and analyzed with the virtual test with CarSim data. The results show that the DCKF algorithm has faster response speed, higher precision of the estimation of the vehicle state, and stronger real-time performance.

Aiming at solving problem of vehicle state estimation, an adaptive fading unscented Kalman filter(AFUKF) algorithm was proposed. Based on this purpose, a 7-DOF nonlinear vehicle model with the Pacejka nonlinear tire model was established firstly. Then, the vehicle state estimator based on Kalman filter was designed to solve the problem of vehicle state estimation. The simulation verification shows the effectiveness and reliability of the designed estimator for vehicle state estimation. Compared with other traditional methods, the calculation accuracy is higher for the AFUKF algorithm to solve the problem of vehicle state estimation. The study can help drivers easily identify key state estimation in safe driving area.

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.