In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using Carim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

With the advancement of intelligent driving technology, researchers are paying more and more attention to the identification of traffic signs. Although a detection method of traffic signs based on color or shape can achieve recognition of large categories of signs such as prohibitions and warnings, the recognition categories are few, and the accuracy is not high. A traffic sign detection algorithm based on color or shape is small in computation and good in real-time, but the color features are greatly affected by light and weather. For the questions raised above, this paper puts forward an improved YOLOv5 method. The method uses the SIoU loss function to take the place of the loss function in the YOLOv5 model, which optimizes the training model, and the convolutional block attention model (CBAM) is fused with the CSP1_3 model in YOLOv5 to form a new CSP1_3CBAM model, which enhances YOLOv5’s feature extraction ability and improves the accuracy regarding traffic signs. In addition, the ACONC is introduced as the activation function of YOLOv5, which promotes YOLOv5’s generalization ability through adaptive selection of activation by linear–nonlinear switching factors. The research results on the TT100k dataset show that the improved YOLOv5 precision rate increased from 73.2% to 81.9%, an increase of 8.7%; the recall rate increased from 74.2% to 77.2%, an increase of 3.0%; and the mAP increased from 75.7% to 81.9%, an increase of 6.2%. The FPS also increased from 26.88 to 30.42 frames per second. The same training was carried out on the GTSDB traffic sign dataset, and the mAP increased from 90.2% to 92.5%, which indicates that the algorithm has good generalization ability.

As position sensorless control technology can avoid many disadvantages caused by mechanical position sensors, improve the reliability of the motor, reduce costs and other advantages, a large number of researchers have conducted research on compound control technology in order to achieve position sensorless control technology in a wide speed range. In this article, the position sensorless compound control technology of a permanent magnet synchronous motor is reviewed, and the compound control technology of a permanent magnet synchronous motor without a position sensor is elaborated. Finally, the existing problems and development trend of sensorless compound control technology are summarized and prospected.

Many tire-road friction coefficient estimation methods require the wheel angular speed sensor to provide a signal. In this paper, a tire-road friction coefficient estimation algorithm for distributed drive electric vehicles using permanent magnet synchronous motors(PMSM) sensorless control is proposed to reduce the use of wheel angular speed sensors. The wheel angular speed signal is replaced by the rotor speed signal obtained from PMSM sensorless control. First, the three degrees of freedom vehicle dynamics model and the PMSM mathematical model are established, and a strong tracking cubature Kalman filtering algorithm based on orthogonal transformation (T-STCKF) is derived to overcome the problem that the STCKF algorithm is prone to nonlocal sampling when dealing with high dimensional systems. Second, a PMSM sensorless control system based on the adaptive exponent reaching law of sliding mode control and the T-STCKF algorithm is established, and confirmed its validity. Finally, the feasibility of the proposed algorithm is demonstrated through multicase simulation and experiment and the results shows that the T-STCKF algorithm is more accurate than the STCKF algorithm.

To accurately obtain the state parameter information of a vehicle, a square root generalized high-order cubature Kalman filter (CKF) estimation algorithm based on the atomic search optimization algorithm (ASO-SRGHCKF) is proposed. On the basis of the high-order CKF, using the generalized cubature rule instead of the cumbersome spherical cubature rule, the algorithm’s weights and cubature points are calculated directly. Then, the square root filtering technology is introduced, and the square root generalized high-order CKF (SRGHCKF) algorithm is derived by replacing the Cholesky decomposition with orthogonal triangle (QR) decomposition. To lessen the estimate error brought on by the noise covariance matrix’s uncertainty, the atomic search optimization (ASO) algorithm is used to optimize it, and the algorithm is utilized for the state parameter estimation of distributed electric vehicles. MATLAB/CarSim cosimulation and experiments evidence that the ASO-SRGHCKF algorithm produces more accurate estimation results and faster convergence than the HCKF algorithm and can precisely obtain the vehicle’s state parameter information.