Abstract: To address the critical challenges of complex dynamic environments, high path planning difficulty, and frequent inter-vehicle conflicts faced by autonomous mining trucks in waste dumps, this study proposes a novel multi-vehicle cooperative parking trajectory planning method based on time-sequence adjustment. The primary objectives are to reduce accident risks and operational costs while significantly enhancing overall productivity. The proposed methodology strategically decomposes this complex problem into two fundamental sub-problems, which are single-vehicle trajectory planning and multi-vehicle conflict resolution, enabling a systematic and efficient solution framework. In the single-vehicle planning module, we specifically address the unique characteristics of mining trucks, including their substantial mass, large dimensions, constraints on terminal posture and constrained maneuverability. The optimization objective is formulated to minimize not only the total travel distance but also the number of reversing maneuvers, steering actions, and direction-switching events, as these operations are particularly time-consuming and energy-intensive for heavy-duty mining trucks. To achieve this, we introduce an innovative direction-switching point sampling method that incorporates both the obstacle distance field information and the precise pose requirements at the trajectory's start and end points. This approach ensures that the generated parking trajectories are not only kinematically feasible and collision-free but also optimized for operational efficiency and vehicle safety. The distance field consideration allows the algorithm to maintain safe margins from dynamic obstacles such as other vehicles, mobile equipment, and changing terrain features, while the pose-based sampling guarantees smooth transitions during direction reversals, reduces the collision risk with other mining trucks that may be caused by multiple direction-switching maneuvers, and effectively reduces mechanical stress on steering systems and tire wear. For multi-vehicle 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 multi-vehicle conflict problem into multiple two-vehicle conflict scenarios, and subsequently achieves low computational cost and efficient conflict resolution by rationally adjusting the operation timing of mining trucks rather than modifying their spatial paths. During the conflict search phase, the algorithm systematically analyzes the planned trajectories of all vehicles to identify potential spatial-temporal conflicts and strategically decouples the complex multi-vehicle problem into a series of more manageable two-vehicle conflict scenarios. The subsequent conflict resolution phase then addresses these pairwise conflicts by intelligently adjusting the operation timing and sequencing of individual mining trucks rather than modifying their pre-planned spatial paths. This time-sequence adjusted based approach is particularly advantageous in waste dump environments where frequent spatial re-planning would be computationally expensive and could introduce new conflicts. The proposed algorithm delivers multiple benefits: it effectively resolves the challenging direction-switching problem for heavy-duty mining trucks, eliminates multi-vehicle conflicts through temporal coordination, and substantially shortens the overall cooperative operation cycle. Comparative analysis experiments were conducted under complicated operational scenarios, including high-density vehicle interactions, dynamic obstacle interferences, and varying terrain conditions, to validate the algorithm's effectiveness. The results demonstrate significant improvements in both collision avoidance and operational efficiency compared to fixed-interval dispatch methods with priority strategy, confirming the superiority and practical applicability of our approach in real-world mining environments.