Abstract: To address the unstructured boundaries, local space constraints, and potential intervehicle conflicts faced by autonomous mining trucks in waste dumps, this study proposes a multivehicle cooperative parking trajectory planning method based on time-sequence adjustment. The primary objectives are to reduce accident risks and operational costs while improving overall operational efficiency. The proposed methodology decomposes this problem into two subproblems: single-vehicle trajectory planning and multivehicle conflict resolution, to enable a systematic and efficient solution framework. In the single-vehicle planning module, we consider the key characteristics of mining trucks, including their large mass, large dimensions, terminal pose constraints, and constrained maneuverability. The planning objective is formulated to reduce the total travel distance as well as the number of reversing maneuvers, steering actions, and direction-switching events, as these operations are time-consuming and energy-intensive for heavy-duty mining trucks. To achieve this, we introduce a direction-switching point sampling method that incorporates both obstacle distance field information and pose requirements at the trajectory's start and end points. This approach helps generate parking trajectories that are kinematically feasible and collision-free while improving operational efficiency and vehicle safety. The distance field consideration allows the algorithm to maintain safe margins from obstacles and dump boundaries, while the pose-based sampling helps reduce unnecessary direction-switching maneuvers and the associated collision risk. For multivehicle conflict resolution, a cooperative algorithm based on time-sequence adjustment is proposed, which adopts a two-stage "conflict search–conflict resolution" strategy. This approach decouples the multivehicle conflict problem into multiple two-vehicle conflict scenarios. It achieves low computational cost and efficient conflict resolution by adjusting the operation timing of mining trucks rather than modifying their spatial paths. During the conflict search phase, the algorithm analyzes the planned trajectories of all vehicles to identify potential spatiotemporal conflicts and decouples the multivehicle problem into a series of more manageable two-vehicle conflict scenarios. The conflict resolution phase then addresses these pairwise conflicts by adjusting the operation timing and sequencing of individual mining trucks rather than modifying their preplanned spatial paths. This time-sequence adjustment-based approach is suitable for waste dump environments, where frequent spatial replanning would be computationally expensive and could introduce additional conflicts. The proposed algorithm effectively addresses the direction-switching problem for heavy-duty mining trucks and eliminates multivehicle conflicts through temporal coordination. Comparative simulation experiments were conducted in waste-dump scenarios involving dense vehicle interactions to validate the algorithm's effectiveness. The results show that, compared with fixed-interval dispatch methods with a priority strategy, the proposed method improves collision avoidance and operational efficiency, demonstrating its effectiveness and potential applicability to cooperative parking operations of autonomous mining trucks in waste dumps.