  In this paper, we present a framework to control a self-driving car by fusing
raw information from RGB images and depth maps. A deep neural network
architecture is used for mapping the vision and depth information,
respectively, to steering commands. This fusion of information from two sensor
sources allows to provide redundancy and fault tolerance in the presence of
sensor failures. Even if one of the input sensors fails to produce the correct
output, the other functioning sensor would still be able to maneuver the car.
Such redundancy is crucial in the critical application of self-driving cars.
The experimental results have showed that our method is capable of learning to
use the relevant sensor information even when one of the sensors fail without
any explicit signal.
