Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints
This work addresses the problem of handling missing values in trajectory data for researchers and practitioners in fields like robotics and autonomous systems, though it is incremental as it complements existing methods rather than introducing a new paradigm.
The paper tackles the challenge of trajectory prediction in dynamic multi-agent environments by introducing Temporal Waypoint Dropping (TWD), which stochastically drops waypoints during training to force learning of temporal dependencies, resulting in substantial improvements in prediction accuracy across multiple datasets.
The inherently diverse and uncertain nature of trajectories presents a formidable challenge in accurately modeling them. Motion prediction systems must effectively learn spatial and temporal information from the past to forecast the future trajectories of the agent. Many existing methods learn temporal motion via separate components within stacked models to capture temporal features. Furthermore, prediction methods often operate under the assumption that observed trajectory waypoint sequences are complete, disregarding scenarios where missing values may occur, which can influence their performance. Moreover, these models may be biased toward particular waypoint sequences when making predictions. We propose a novel approach called Temporal Waypoint Dropping (TWD) that explicitly incorporates temporal dependencies during the training of a trajectory prediction model. By stochastically dropping waypoints from past observed trajectories, the model is forced to learn the underlying temporal representation from the remaining waypoints, resulting in an improved model. Incorporating stochastic temporal waypoint dropping into the model learning process significantly enhances its performance in scenarios with missing values. Experimental results demonstrate our approach's substantial improvement in trajectory prediction capabilities. Our approach can complement existing trajectory prediction methods to improve their prediction accuracy. We evaluate our proposed approach on three datasets: NBA Sports VU, ETH-UCY, and TrajNet++.