Spatiotemporal Coordination of Intelligent Autonomous Vehicles in Automated Container Terminals Using Deep Reinforcement Learning

Capacity-limited lanes and intersections constrain the efficiency of intelligent autonomous vehicles (IAVs) in automated container terminals (ACTs). Although onboard sensing and traffic monitoring can provide vehicle state information, translating this information into coordinated routing and intersection decisions remains challenging. Therefore, this study proposes a two-layer coordination framework that integrates path planning with adaptive platoon size control at intersections. The first layer develops a path planning method based on a conflict point network. By dynamically updating continuous timelines of conflict points to record spatiotemporal occupancy states, this method establishes key feasibility constraints that ensure collision-free routing and minimize the task completion time of IAVs. The second layer employs a Deep Q-Network (DQN) to adjust intersection platoon sizes according to current platoon configurations and traffic performance feedback. The framework operates on processed traffic states that could be supplied by vehicle localization and monitoring systems. This learning-driven control mechanism enhances intersection throughput, reduces congestion, and enables adaptive coordination in dynamic operating environments. The proposed framework is evaluated under varying traffic demand scenarios. Compared to traditional rule-based methods, experimental results demonstrate that it reduces average delay by up to 34.45% at isolated intersections and 27.60% in network-wide settings. These findings highlight the effectiveness of the proposed framework in improving IAV operations and offer a scalable solution to support future port automation and intelligent transportation management.