In traditional parking control, the pure pursuit algorithm has certain limitations due to discontinuities in the path and a fixed look-ahead distance, particularly in terms of tracking accuracy and smoothness. To address the issues of selecting the lookahead distance, poor endpoint performance, and front wheel angle jitter in the pure pursuit method for parking scenarios, a parking trajectory tracking method based on path virtualization and adaptive preview is proposed. Firstly, the geometric relationship model using the pure pursuit method is analyzed. On this basis, optimization strategies for endpoint handling and preprocessing of the parking trajectory path are introduced. These strategies address the issues of oscillation caused by discontinuous curvature in the parking trajectory and jitter resulting from front wheel angle changes near the endpoint by virtually extending and simulating the tracking of the parking trajectory. Furthermore, an adaptive curve preview distance strategy is proposed to reduce the variation amplitude of the front wheel angle during parking, thereby mitigating oscillations and enhancing the tracking accuracy of the parking trajectory. Finally, the implementation steps of the proposed method are presented. The testing and validation are conducted. Compared with the unmodified pure pursuit algorithm, simulation, and real-vehicle test results show that the proposed method exhibits superior tracking performance and endpoint accuracy, effectively reducing the jitter caused by the front wheel angle during parking tracks. For the proposed algorithm, a performance evaluation index matrix, including maximum lateral error, parking endpoint distance error, and cumulative front wheel angle oscillation value and mean difference, indicates average performance improvements of 54.08%, 83.61%, 71.34%, and 48.95%, respectively. These results highlight its effectiveness and practical application value.