LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction

Abstract

Multi-agent trajectory prediction is a fundamental problem in autonomous driving. The key challenges in prediction are accurately anticipating the behavior of surrounding agents and understanding the scene context. To address these problems, we propose LatentFormer, a transformerbased model for predicting future vehicle trajectories. The proposed method leverages a novel technique for modeling interactions among dynamic objects in the scene. Contrary to many existing approaches which model cross-agent interactions during the observation time, our method additionally exploits the future states of the agents. This is accomplished using a hierarchical attention mechanism where the evolving states of the agents autoregressively control the contributions of past trajectories and scene encodings in the final prediction. Furthermore, we propose a multiresolution map encoding scheme that relies on a vision transformer module to effectively capture both local and global scene context to guide the generation of more admissible future trajectories. We evaluate the proposed method on the nuScenes benchmark dataset and show that our approach achieves state-of-the-art performance and improves upon trajectory metrics by up to 40%. We further investigate the contributions of various components of the proposed technique via extensive ablation studies.

LTransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing

“…Recent research proves that the Transformer outperforms other deep learning methods in trajectory prediction. Amirloo et al [16] proposed LatentFormer, a transformerbased model able to predict future vehicle trajectories by leveraging a novel technique to model interactions among dynamic objects in the scene. Accounting for the interaction between vehicles, Yan et al [17] proposed two spatial attention mechanisms to help the model understand the surrounding environment better and thus improve its prediction accuracy.…”