Leveraging Spatial Context for Improved Long-Term Predictions with Swin Transformers

摘要

Trajectory prediction is a critical task for autonomous systems such as self-driving cars, surveillance systems, and social robots. The goal is to predict the future paths of road users, including cars, bikes, and pedestrians, by using their historical movement patterns and the surrounding environment. While traditional models based on Newton’s laws and social interaction forces have been used in the past, data-driven methods have become essential for learning complex spatial and temporal interactions between pedestrians. In this work, we propose SwYn-Net, a model for predicting long-term trajectories of pedestrians that can handle the uncertainty of multiple plausible paths and address the accumulation of errors over time. Our approach uses a shifted-window attention mechanism to capture the scene’s local and global context. We evaluate our model on the SDD, inD and the MOT20 datasets and show that SwYn-Net can be an efficient model to predict short-term and long-term trajectories.