  Self-driving technology is advancing rapidly --- albeit with significant
challenges and limitations. This progress is largely due to recent developments
in deep learning algorithms. To date, however, there has been no systematic
comparison of how different deep learning architectures perform at such tasks,
or an attempt to determine a correlation between classification performance and
performance in an actual vehicle, a potentially critical factor in developing
self-driving systems. Here, we introduce the first controlled comparison of
multiple deep-learning architectures in an end-to-end autonomous driving task
across multiple testing conditions. We compared performance, under identical
driving conditions, across seven architectures including a fully-connected
network, a simple 2 layer CNN, AlexNet, VGG-16, Inception-V3, ResNet, and an
LSTM by assessing the number of laps each model was able to successfully
complete without crashing while traversing an indoor racetrack. We compared
performance across models when the conditions exactly matched those in training
as well as when the local environment and track were configured differently and
objects that were not included in the training dataset were placed on the track
in various positions. In addition, we considered performance using several
different data types for training and testing including single grayscale and
color frames, and multiple grayscale frames stacked together in sequence. With
the exception of a fully-connected network, all models performed reasonably
well (around or above 80\%) and most very well (~95\%) on at least one input
type but with considerable variation across models and inputs. Overall,
AlexNet, operating on single color frames as input, achieved the best level of
performance (100\% success rate in phase one and 55\% in phase two) while
VGG-16 performed well most consistently across image types.
