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.

翻译：在复杂环境中预测动态智能体的未来轨迹对于自动驾驶、机器人技术及人机交互等应用至关重要。由于智能体的行为具有未知性和内在多模态特性，这是一项具有挑战性的任务。我们的核心见解在于：智能体的行为不仅受其历史轨迹及与即时环境交互的影响，还很大程度上受其长期航路点（LTW）的制约。本文研究了添加长期目标对轨迹预测框架性能的影响。我们提出了一种可解释的长期航路点驱动预测框架（WayDCM）。WayDCM首先通过联合使用离散选择模型（DCM）和神经网络模型（NN）对智能体与环境交互及其LTW进行编码，预测其中间目标（IG），随后预测对应的轨迹。这与先前未考虑智能体终极意图进行轨迹预测的工作形成鲜明对比。我们在Waymo开放数据集上验证了该方法的有效性。