  Deep learning-based approaches have been widely used for training controllers
for autonomous vehicles due to their powerful ability to approximate nonlinear
functions or policies. However, the training process usually requires large
labeled data sets and takes a lot of time. In this paper, we analyze the
influences of features on the performance of controllers trained using the
convolutional neural networks (CNNs), which gives a guideline of feature
selection to reduce computation cost. We collect a large set of data using The
Open Racing Car Simulator (TORCS) and classify the image features into three
categories (sky-related, roadside-related, and road-related features).We then
design two experimental frameworks to investigate the importance of each single
feature for training a CNN controller.The first framework uses the training
data with all three features included to train a controller, which is then
tested with data that has one feature removed to evaluate the feature's
effects. The second framework is trained with the data that has one feature
excluded, while all three features are included in the test data. Different
driving scenarios are selected to test and analyze the trained controllers
using the two experimental frameworks. The experiment results show that (1) the
road-related features are indispensable for training the controller, (2) the
roadside-related features are useful to improve the generalizability of the
controller to scenarios with complicated roadside information, and (3) the
sky-related features have limited contribution to train an end-to-end
autonomous vehicle controller.
