Sensor fusion is a key technology that integrates various sensory inputs to allow for robust decision making in many applications such as autonomous driving and robot control. Deep neural networks have been adopted for sensor fusion in a body of recent studies. Among these, the so-called netgated architecture was proposed, which has demonstrated improved performances over the conventional convolutional neural networks (CNN). In this paper, we address several limitations of the baseline negated architecture by proposing two further optimized architectures: a coarser-grained gated architecture employing (feature) group-level fusion weights and a two-stage gated architectures leveraging both the group-level and feature level fusion weights. Using driving mode prediction and human activity recognition datasets, we demonstrate the significant performance improvements brought by the proposed gated architectures and also their robustness in the presence of sensor noise and failures.

本文提出两种更优化的结构: 一种采用更粗粒度的门控机制，使用组级别融合权重; 另一种是使用组级别和特征级别的融合权重的两阶段门控机制，实验结果表明所提出的门控结构在面对传感器噪声和故障时具有鲁棒性并显著提高了性能。

传感器融合优化门控深度学习架构