a safety navigation method integrating global path planning and local obstacle avoidance for self-driving cars in a dynamic environment

In this paper，a novel method for obtaining high-quality paths for self-driving cars in underground parking lots is proposed. self-driving cars require fast and accurate planning of collisionless path. when the self-driving car arrives at the parking lot, the car downloads the layout from the intelligent system of the parking lot and is assigned a parking space, then the location of the designated parking space and the car are provided by the intelligent system. a global path is planned by the global algorithm according to the location of the parking space and the car. if dynamic or unknown obstacles are detected in the process of moving along the global path, the parameters of obstacles can be estimated by the obstacle-detection algorithm. according to obtained parameters, the local obstacle avoidance path can be planned by the behavioral dynamics method. after completing obstacle avoidance, then the car will return to the global path and continue to move toward the target parking space. finally, the proposed method is simulated by matlab, and the results show that the car can safely park in the target parking space. this method simultaneously satisfies the smooth and the real-time requirements of path planning.