  Predicting the future trajectories of pedestrians on the road is an important
task for autonomous driving. The pedestrian trajectory prediction is affected
by scene paths, pedestrian's intentions and decision-making, which is a
multi-modal problem. Most recent studies use past trajectories to predict a
variety of potential future trajectory distributions, which do not account for
the scene context and pedestrian targets. Instead of predicting the future
trajectory directly, we propose to use scene context and observed trajectory to
predict the goal points first, and then reuse the goal points to predict the
future trajectories. By leveraging the information from scene context and
observed trajectory, the uncertainty can be limited to a few target areas,
which represent the "goals" of the pedestrians. In this paper, we propose
GoalNet, a new trajectory prediction neural network based on the goal areas of
a pedestrian. Our network can predict both pedestrian's trajectories and
bounding boxes. The overall model is efficient and modular, and its outputs can
be changed according to the usage scenario. Experimental results show that
GoalNet significantly improves the previous state-of-the-art performance by
48.7% on the JAAD and 40.8% on the PIE dataset.
