Aiming at the problems of large memory consumption, long operation time and low security of traditional A * algorithm, a dynamic adaptive improved algorithm based on A * algorithm is proposed. Firstly, according to the heuristic function of the proportion of obstacles in the map, the adaptive dynamic weighting is carried out to shorten the search time. Secondly, the jump point search (JPS) algorithm is integrated to remove a large number of redundant points and improve the running speed of the algorithm. Finally, according to the relative position of the starting point and the target point, the obstacles are expanded dynamically to keep a safe distance between the planned path and the obstacles. In order to verify the effect, the improved algorithm is tested in different sizes and different proportion of obstacles. The results show that the improved algorithm is efficient and safe in global path planning.

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In the process of obstacle avoidance, there are some problems such as low precision and poor safety in the path tracking of intelligent vehicles. To solve these problems, a model prediction controller with soft constraints is designed in this paper. The controller adopts the vehicle motion model, according to the radius of the circumcircle of the obstacle and the vehicle, and the anti-collision constraint condition is constructed to improve vehicle safety. The vehicle tracks its path using a model predictive control (MPC) algorithm. Sideslip angle and acceleration control variables were introduced as soft constraints to improve vehicle path tracking accuracy. The simulation was carried out by CarSim and MATLAB/Simulink platforms. The results show that the constrained MPC control algorithm is advanced in the accuracy and stability of trajectory tracking control under high-speed obstacle avoidance.