Intelligent scheduling control for coordinated passing of multiple autonomous trucks at intersections in open-pit coal mines via enhanced multi-agent reinforcement learning

Abstract The intelligent scheduling of autonomous trucks at intersections in open-pit coal mines poses a fundamental challenge due to nonlinear dynamics and complex multi-truck interactions under gradient–load coupling. Unlike existing intersection control studies that are mainly designed for flat urban roads and neglect mining-specific physical constraints, this study proposes a Gradient-Load Aware Multi-Agent Attention-Enhanced DDPG (GLA-MA-MADDPG) approach that explicitly embeds mining-domain physical laws into multi-agent reinforcement learning for intersection scheduling. A gradient–load dynamic coupling model is developed to inject physics fidelity into MARL-based intersection scheduling, capturing nonlinear and load-sensitive kinematics overlooked by flat-road assumptions in prior studies. To handle coordination under these constraints, we design a task-oriented multi-dimensional attention mechanism that jointly interprets physical heterogeneity, asymmetric dynamics, priorities, and collision risks. Additionally, a gradient-aware adaptive priority strategy redefines right-of-way as a physics-grounded, state-dependent process, ensuring safe and preferential passage for loaded and downhill trucks. In the simulated four-branch gradient intersection environment, the proposed GLA-MA-MADDPG method achieves substantial performance gains over traditional MADDPG across 20 independent runs. Specifically, it improves throughput by 23.5%, reduces average transit time by 8.3%, decreases waiting time by 31.2%, and lowers the collision rate from 3.47‰ to 1.32%, achieving efficiency gains of 39.2% in mixed uphill–downhill scenarios. Overall, this study contributes a first-of-its-kind integration of physics-informed gradient–load modeling with attention-driven MARL, providing a generalizable computational framework for intelligent intersection scheduling in open-pit mining and other large-scale, safety-critical engineering systems.