  In this work we predict vehicle speed and steering angle given camera image
frames. Our key contribution is using an external pre-trained neural network
for segmentation. We augment the raw images with their segmentation masks and
mirror images. We ensemble three diverse neural network models (i) a CNN using
a single image and its segmentation mask, (ii) a stacked CNN taking as input a
sequence of images and segmentation masks, and (iii) a bidirectional GRU,
extracting image features using a pre-trained ResNet34, DenseNet121 and our own
CNN single image model. We achieve the second best performance for MSE angle
and second best performance overall, to win 2nd place in the ICCV Learning to
Drive challenge. We make our models and code publicly available.
