  Algorithms that fuse multiple input sources benefit from both complementary
and shared information. Shared information may provide robustness against
faulty or noisy inputs, which is indispensable for safety-critical applications
like self-driving cars. We investigate learning fusion algorithms that are
robust against noise added to a single source. We first demonstrate that
robustness against single source noise is not guaranteed in a linear fusion
model. Motivated by this discovery, two possible approaches are proposed to
increase robustness: a carefully designed loss with corresponding training
algorithms for deep fusion models, and a simple convolutional fusion layer that
has a structural advantage in dealing with noise. Experimental results show
that both training algorithms and our fusion layer make a deep fusion-based 3D
object detector robust against noise applied to a single source, while
preserving the original performance on clean data.
