In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized.

Aiming at precisely tracking an intelligent vehicle on a desired trajectory, this paper proposes an intelligent vehicle trajectory planning and control strategy based on an improved terminal sliding mold. Firstly, the traditional RRT algorithm is improved by using the target bias strategy and the separation axis theorem to improve the algorithm search efficiency. Secondly, an improved terminal sliding mode controller is designed. The controller comprehensively considers the lateral error and heading error of the tracking control, and the stability of the control system is proven by the Lyapunov function. Finally, the performance of the designed controller is verified by the Matlab-Carsim HIL simulation platform. The test results of the Matlab-Carsim HIL simulation platform show that, compared with the general terminal sliding mode controller, the improved terminal sliding mode controller designed in this paper has higher control accuracy and better robustness.

Electrochemical energy storage technology has the characteristics of convenient use, fast response, and flexible configuration. At present, the energy storage technology used in smart electric vehicles is mainly electrochemical energy storage technology. In particular, the promotion of electrochemical energy storage technology in the field of smart electric vehicles is an effective way to achieve the goal of carbon neutrality. One of the most critical issues limiting the development and popularity of intelligent electric vehicles is the performance and range of power batteries; vehicle path planning is very important to the performance of power batteries and the driving range. Improved path planning algorithms can obviously shorten the path length and reduce the time of searching and planning a path under the condition of the same starting point and end point, that is, to increase the range of the power battery. On the premise of the comprehensive analysis of the intelligent electric vehicle’s grasp of environmental information, trajectory planning methods are divided into local trajectory planning and global trajectory planning methods. The main content of the trajectory planning method is given, the key technologies involved in the research are discussed, and its advantages and disadvantages are analyzed. Finally, the main development trends of intelligent electric vehicle trajectory planning technology in the future are proposed.