Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A ∗ algorithm. First, this method improves the heuristic function of the traditional A ∗ algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved A ∗ algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified A ∗ algorithm and the traditional A ∗ algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.

In this paper, an improved bidirectional RRT ∗ vehicle path planning method for smart vehicle is proposed. In this method, the resultant force of the artificial potential field is used to determine the search direction to improve the search efficiency. Different kinds of constraints are considered in the method, including the vehicle constraints and the vehicle driving environment constraints. The collision detection based on separating axis theorem is used to detect the collision between the vehicle and the obstacles to improve the planning efficiency. The cubic B-spline curve is used to optimize the path to make the path’s curvature continuous. Both simulation and experiment are implemented to verify the proposed improved bidirectional RRT ∗ method. In the simulation analysis, this paper’s method can generate the smoothest path and takes the shortest time compared with the other two methods and it can be adaptive to the complicated environment. In the real vehicle experiment, we can see from the test results that this paper’s method can be applied in practice on the smart electric vehicle platform; compared with others’ algorithm, this paper’s algorithm can generate shortest and smoothest path.

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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.