LTMSformer: A Local Trend-Aware Attention and Motion State Encoding Transformer for Multi-Agent Trajectory Prediction

• URL: http://arxiv.org/abs/2507.04634v1
• Date: Mon, 07 Jul 2025 03:33:14 GMT
• Title: LTMSformer: A Local Trend-Aware Attention and Motion State Encoding Transformer for Multi-Agent Trajectory Prediction
• Authors: Yixin Yan, Yang Li, Yuanfan Wang, Xiaozhou Zhou, Beihao Xia, Manjiang Hu, Hongmao Qin,
• Abstract summary: We propose a lightweight framework, LTMSformer, to extract temporal-spatial interaction features for trajectory prediction.<n>We also build a Motion State to incorporate high-order motion state attributes, such as acceleration, jerk, heading, etc.<n>Experiment results show that our method outperforms the baseline HiVT-64, reducing the minADE by approximately 4.35%, the minFDE by 8.74%, and the MR by 20%.
• Score: 6.520837230073969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has been challenging to model the complex temporal-spatial dependencies between agents for trajectory prediction. As each state of an agent is closely related to the states of adjacent time steps, capturing the local temporal dependency is beneficial for prediction, while most studies often overlook it. Besides, learning the high-order motion state attributes is expected to enhance spatial interaction modeling, but it is rarely seen in previous works. To address this, we propose a lightweight framework, LTMSformer, to extract temporal-spatial interaction features for multi-modal trajectory prediction. Specifically, we introduce a Local Trend-Aware Attention mechanism to capture the local temporal dependency by leveraging a convolutional attention mechanism with hierarchical local time boxes. Next, to model the spatial interaction dependency, we build a Motion State Encoder to incorporate high-order motion state attributes, such as acceleration, jerk, heading, etc. To further refine the trajectory prediction, we propose a Lightweight Proposal Refinement Module that leverages Multi-Layer Perceptrons for trajectory embedding and generates the refined trajectories with fewer model parameters. Experiment results on the Argoverse 1 dataset demonstrate that our method outperforms the baseline HiVT-64, reducing the minADE by approximately 4.35%, the minFDE by 8.74%, and the MR by 20%. We also achieve higher accuracy than HiVT-128 with a 68% reduction in model size.

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