This invention relates to the field of real-time
resource scheduling technology, specifically a real-time scheduling method for lane control robots based on multi-dimensional sensor data. The method includes: dividing management areas for arrival and drop-off robots; acquiring multimodal sensor data from the management areas, extracting features, and fusing them using a self-attention mechanism to obtain a fused
feature vector; obtaining a drop-off
scenario complexity
score and a departure intention
score through a drop-off
scenario evaluation model; simultaneously calculating the real-time resource occupancy index for each lane; calculating differentiated allowed dwell
time based on the drop-off
scenario complexity
score and the overall resource occupancy status of the drop-off platform; and providing early warnings and management based on the drop-off scenario complexity score, departure intention score, real-time resource occupancy index, differentiated allowed dwell time, and empty space detection results. This invention improves flexible decision-making capabilities and
resource utilization, and is suitable for real-time
resource allocation and decision support in complex traffic scenarios.