DSFormer-RTP: Dynamic-stream Transformers for Real-time Deterministic Trajectory Prediction

Abstract

As delivery robots are increasingly integrated into our daily lives, their ability to navigate through crowded spaces demands swift and accurate prediction of pedestrian trajectories, which is crucial for autonomous functionality. However, existing methods face challenges of unstable accuracy and inefficiency in real-world deployment. Trajectory prediction involves both temporal and social dimensions. Recent methods have achieved better results by modeling temporal and social dimensions simultaneously, preventing information loss compared to modeling them separately, which significantly increases computational costs, posing challenges for practical deployment.In this paper, we conceptualize the trajectory prediction task as a deterministic sequence-to-sequence model that produces one precise forecast, aligning with real-world needs while reducing complexity. To improve efficiency and reduce latency for real-time applications, we propose a novel dynamic-stream transformer architecture that categorizes layers into multi-stream and single-stream based on the number of dimensions involved in computation. The single-stream modules attend to all dimensions simultaneously, providing comprehensive information fusion but with higher computational complexity. The multi-stream modules focus on only one dimension, enabling parallel and batched computation, crucial for improving the model’s real-time performance. By combining them strategically, we achieve a balance between accuracy and speed. Extensive experiments on real datasets show that our dynamic-stream transformer architecture significantly reduces computational complexity, achieving a speed increase of 180% to 3180% compared to similar approaches, while also attaining performance close to the state-of-the-art (SOTA) for deterministic trajectory prediction.