HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention

Xiaolong Tang, Meina Kan, Shiguang Shan, Zhilong Ji, Jinfeng Bai, Xilin Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15261-15270

Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm which predicts the future trajectory by using a fixed duration of historical frames. These methods make the predictions independently even at adjacent time steps which leads to potential instability and temporal inconsistency. As successive time steps have largely overlapping historical frames their forecasting should have intrinsic correlation such as overlapping predicted trajectories should be consistent or be different but share the same motion goal depending on the road situation. Motivated by this in this work we introduce HPNet a novel dynamic trajectory forecasting method. Aiming for stable and accurate trajectory forecasting our method leverages not only historical frames including maps and agent states but also historical predictions. Specifically we newly design a Historical Prediction Attention module to automatically encode the dynamic relationship between successive predictions. Besides it also extends the attention range beyond the currently visible window benefitting from the use of historical predictions. The proposed Historical Prediction Attention together with the Agent Attention and Mode Attention is further formulated as the Triple Factorized Attention module serving as the core design of HPNet. Experiments on the Argoverse and INTERACTION datasets show that HPNet achieves state-of-the-art performance and generates accurate and stable future trajectories. Our code are available at https://github.com/XiaolongTang23/HPNet.