  A promising approach to autonomous driving is machine learning. In such
systems, training datasets are created that capture the sensory input to a
vehicle as well as the desired response. A disadvantage of using a learned
navigation system is that the learning process itself may require a huge number
of training examples and a large amount of computing. To avoid the need to
collect a large training set of driving examples, we describe a system that
takes advantage of the huge number of training examples provided by ImageNet,
but is able to adapt quickly using a small training set for the specific
driving environment.
