  An active area of research is to increase the safety of self-driving
vehicles. Although safety cannot be guarenteed completely, the capability of a
vehicle to predict the future trajectories of its surrounding vehicles could
help ensure this notion of safety to a greater deal. We cast the trajectory
forecast problem in a multi-time step forecasting problem and develop a
Convolutional Neural Network based approach to learn from trajectory sequences
generated from completely raw dataset in real-time. Results show improvement
over baselines.
