Trajectory prediction is an important task that involves modeling the
indeterminate nature of traffic actors to forecast future trajectories given
the observed trajectory sequences. However, current methods confine themselves
to presumed data manifolds, assuming that trajectories strictly adhere to these
manifolds, resulting in overly simplified predictions. To this end, we propose
a novel approach called SSWNP (Self-Supervised Waypoint Noise Prediction). In
our approach, we first create clean and noise-augmented views of past observed
trajectories across the spatial domain of waypoints. We then compel the
trajectory prediction model to maintain spatial consistency between predictions
from these two views, in addition to the trajectory prediction task.
Introducing the noise-augmented view mitigates the model's reliance on a narrow
interpretation of the data manifold, enabling it to learn more plausible and
diverse representations. We also predict the noise present in the two views of
past observed trajectories as an auxiliary self-supervised task, enhancing the
model's understanding of the underlying representation and future predictions.
Empirical evidence demonstrates that the incorporation of SSWNP into the model
learning process significantly improves performance, even in noisy
environments, when compared to baseline methods. Our approach can complement
existing trajectory prediction methods. To showcase the effectiveness of our
approach, we conducted extensive experiments on three datasets: NBA Sports VU,
ETH-UCY, and TrajNet++, with experimental results highlighting the substantial
improvement achieved in trajectory prediction tasks.

通过引入噪声增强的观测轨迹视图和自我监督任务，提出了一种名为 SSWNP（自我监督的航点噪声预测）的新方法，该方法能改善轨迹预测模型的性能并提供更合理且多样的表示。实验证据表明，我们的方法在嘈杂环境中相对于基准方法显著提升了性能，可与现有的轨迹预测方法相辅相成。