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.

在复杂环境中预测动态实体的未来轨迹对于自动驾驶、机器人技术和人机交互等领域至关重要。本研究中，我们研究了在轨迹预测框架中加入长期目标对性能的影响，并提出了一种可解释的长期目标驱动预测框架 (WayDCM)，通过编码他们与环境的互动以及长期目标，首先预测了代理人的中间目标，然后预测相应的轨迹。