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 convolu- tional 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 recogni- tion 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.

Optimized gated deep learning architectures for sensor fusion is proposed. The authors improve upon several limitations of the baseline negated architecture by proposing a coarser-grained gated fusion architecture and a two-stage gated fusion architecture Proposes two gated deep learning architectures for sensor fusion and by having the grouped features, demonstrates improved performance, especially in the presence of random sensor noise and failures.