A Method and System for Online Intent Recognition in Unmanned Clusters Integrating Self-Attention Models

Abstract

This disclosure provides a method and system for online intent recognition of unmanned swarms based on a self-attention model. First, depth images collected by the onboard equipment of the unmanned system are acquired, and the 3D bounding box of each target at the current moment is determined as the current detection target. Prior predictions are obtained based on the historical trajectories of the tracked unmanned swarm targets. The similarity between the current detection target and the prior predictions is measured from the target position, 3D shape, point cloud quantity, and spatial location to achieve cross-frame data association. A Transformer model based on a multi-head self-attention mechanism is used to process the associated historical trajectory sequence, jointly outputting the probability distribution of multiple intents of the swarm targets, and the short-term predicted position and bounding box size for each intent. This invention can achieve joint intent-trajectory prediction when facing concurrent multi-target swarms; and through an effective matching mechanism, it reduces the impact of interference on cross-frame data association, effectively reducing the false positive and false negative rates.

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