Self-Attention Sensor Fusion for Dynamic Adaptation

The patent applies the dynamics principle by transforming the static attention mechanism into a dynamic one. The self-attention mechanism computes attention weights dynamically based on the current input features, allowing the model to adapt to fluctuating conditions where different sensors become important at different times. The attention weights are recalculated for each input, enabling the system to focus on relevant sensors according to the current situation rather than using fixed pre-trained parameters.

The patent applies parameter changes by transforming fixed integration parameters into dynamic parameters. Instead of using fixed weights in sum integration or simple selection in maximal integration, the self-attention mechanism generates dynamic attention weights based on the input features. These weights are learned parameters that automatically adjust to reflect the importance of different sensors in different situations, improving integration accuracy while adapting to changing conditions.

The patent applies the self-service principle by enabling the model to determine its own attention weights without relying on pre-trained fixed parameters. The self-attention mechanism allows the model to serve itself by automatically learning which sensors are important for each situation during training, and then applying this learned capability during inference. This self-determining capability improves estimation accuracy while the model learns to focus on relevant sensors based on the input context.