  Predicting the future trajectories of dynamic agents in complex environments
is crucial for a variety of applications, including autonomous driving,
robotics, and human-computer interaction. It is a challenging task as the
behavior of the agent is unknown and intrinsically multimodal. Our key insight
is that the agents behaviors are influenced not only by their past trajectories
and their interaction with their immediate environment but also largely with
their long term waypoint (LTW). In this paper, we study the impact of adding a
long-term goal on the performance of a trajectory prediction framework. We
present an interpretable long term waypoint-driven prediction framework
(WayDCM). WayDCM first predict an agent's intermediate goal (IG) by encoding
his interactions with the environment as well as his LTW using a combination of
a Discrete choice Model (DCM) and a Neural Network model (NN). Then, our model
predicts the corresponding trajectories. This is in contrast to previous work
which does not consider the ultimate intent of the agent to predict his
trajectory. We evaluate and show the effectiveness of our approach on the Waymo
Open dataset.
