To address the limitations of traditional trajectory planning algorithms in confined spaces, such as low planning success rates, poor adaptability, and deviations from human driving habits, this paper proposes an adaptive two-stage trajectory planning algorithm integrated with virtual obstacle decision-making. The first stage combines dynamic programming and quadratic programming to achieve path planning and velocity optimization for autonomous vehicles. Subsequently, an adaptive aggregation sampling strategy is introduced to resolve navigation challenges in narrow environments. Finally, a random forest-based virtual obstacle decision model is developed to enhance decision-making rationality under diverse vehicle interaction scenarios. The results on the simulation platform Carla show that, compared with the traditional method, the path length and path curvature of the proposed method are reduced by 2.4% and 85.6% respectively, and the success rate of planning, safety and stability are improved by about 20%, 20.6% and 44.9% respectively in the static multi-obstacle avoidance in narrow areas. In the dynamic multi-obstacle avoidance in narrow areas, the path length and path curvature are reduced by 8.3% and 76.4%, respectively, and the planning success rate, safety and stability are improved by about 36%, 78.2% and 45.3%, respectively. Finally, the method was deployed to the actual unmanned vehicle, and the obstacles were set up in the narrow and long corridor scene for testing, which verified the effectiveness of the method.