  Predicting vehicle trajectories, angle and speed is important for safe and
comfortable driving. We demonstrate the best predicted angle, speed, and best
performance overall winning the top three places of the ICCV 2019 Learning to
Drive challenge. Our key contributions are (i) a general neural network system
architecture which embeds and fuses together multiple inputs by encoding, and
decodes multiple outputs using neural networks, (ii) using pre-trained neural
networks for augmenting the given input data with segmentation maps and
semantic information, and (iii) leveraging the form and distribution of the
expected output in the model.
