Abstract: Aiming at obstacle avoidance path planning for intelligent vehicles on structured roads, this paper proposes an obstacle avoidance path planning method based on sampling area optimization. Fully considering the road environment and obstacle vehicle information, it establishes an obstacle vehicle expansion ellipse layer model to divide the risk of the road environment. The obstacle avoidance process is divided into three stages: changing lanes to avoid obstacles, going straight after changing lanes, and returning to the global reference route. In each stage, low-risk sampling points are selected to generate candidate paths through the expansion elliptic layer model. Considering path comfort and global path tracking abilities, this paper designs the comfort and offset cost functions, and combines the expansion ellipse layer model to establish the safety cost function and select the optimal path. In order to test the effectiveness of the method, the obstacle avoidance simulation and real vehicle verification are carried out by constructing straight roads and curve scenes. The research results show that under different scenarios, the proposed method can effectively avoid collision with static and dynamic obstacle vehicles, and efficiently plan safe and smooth driving paths.