  Autonomous driving has a significant impact on society. Predicting vehicle
trajectories, specifically, angle and speed, is important for safe and
comfortable driving. This work focuses on fusing inputs from camera sensors and
visual map data which lead to significant improvement in performance and plays
a key role in winning the challenge. We use pre-trained CNN's for processing
image frames, a neural network for fusing the image representation with visual
map data, and train a sequence model for time series prediction. We demonstrate
the best performing MSE angle and best performance overall, to win the ICCV
2019 Learning to Drive challenge. We make our models and code publicly
available.
