A Lightweight Trajectory Prediction Model with a Spatio-Temporal Factorized Encoder and a Heterogeneous Decoder

LIN Qinghua; WANG Meifang; LI Hui; GE Tongao; YAN Jun

doi:10.12263/DZXB.20260073

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A Lightweight Trajectory Prediction Model with a Spatio-Temporal Factorized Encoder and a Heterogeneous Decoder

PAPERS | 更新时间：2026-07-30

- A Lightweight Trajectory Prediction Model with a Spatio-Temporal Factorized Encoder and a Heterogeneous Decoder
- ACTA ELECTRONICA SINICA Vol. 54, Issue 4, Pages: 1748-1760(2026)
- 作者机构：
  
  1.青岛科技大学数据科学学院，山东青岛 266100
  2.武汉理工大学计算机与人工智能学院，湖北武汉 430070
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2023YFF0612100);Natural Science Foundation of Shandong Province(ZR2024MF023);Key Research and Development Program of Shandong Province(2024TSGC0170)
- DOI：10.12263/DZXB.20260073
  CLC： TP391;
- Received：27 February 2026，
  
  Accepted：08 April 2026，
  
  Online First：22 May 2026，
  
  Published：25 April 2026
- 稿件说明：
移动端阅览
林清华, 王美芳, 李辉, 等. 基于时空维度分解与异构解码的轻量化轨迹预测模型[J]. 电子学报, 2026, 54(04): 1748-1760.

LIN Qinghua, WANG Meifang, LI Hui, et al. A Lightweight Trajectory Prediction Model with a Spatio-Temporal Factorized Encoder and a Heterogeneous Decoder[J]. Acta Electronica Sinica, 2026, 54(04): 1748-1760.
林清华, 王美芳, 李辉, 等. 基于时空维度分解与异构解码的轻量化轨迹预测模型[J]. 电子学报, 2026, 54(04): 1748-1760. DOI：10.12263/DZXB.20260073

LIN Qinghua, WANG Meifang, LI Hui, et al. A Lightweight Trajectory Prediction Model with a Spatio-Temporal Factorized Encoder and a Heterogeneous Decoder[J]. Acta Electronica Sinica, 2026, 54(04): 1748-1760. DOI：10.12263/DZXB.20260073

摘要

高精度与高效率的轨迹预测模型是自动驾驶系统实现安全导航的关键。现有方法多采用图神经网络（Graph Neural Networks，GNNs）或注意力机制联合建模智能体与高精地图之间的时空依赖关系，虽然在预测精度上取得了显著进展，但其耦合式的时空建模方式导致计算复杂度过高，严重限制了模型在车载嵌入式系统等实际平台上的部署能力。此外，目前主流方法在解码阶段普遍采用单一的参数化解码范式处理场景中所有智能体，忽视了不同智能体在预测精度与多模态性方面的差异化需求，造成计算资源的低效分配。为此，本文提出一种基于时空维度分解与异构解码的轻量化轨迹预测模型（lightweight trajectory prediction Model based on spatio-temporal dimension decomposition and heterogeneous decoding，MAM-M），该模型由时空维度分解编码器与异构解码器两大核心模块构成。在编码阶段，针对传统Mamba模型在长序列建模中存在的单向信息衰减问题，设计了时序双流Mamba网络，通过正向与反向双流并行处理历史轨迹，使每个时间步同时包含完整的过去与未来信息，有效缓解了长程遗忘问题，并利用线性复杂度技术实现了时间维度上的高效编码。随后，采用自注意力机制在空间维度

上捕捉智能体与高精地图之间的全局交互关系，从而实现“先时间后空间”的解耦编码策略，显著减少了时空维度上的冗余计算。在解码阶段，提出一种异构解码器以适配不同智能体的预测需求：对运动复杂、行为不确定性高的目标智能体，采用基于可学习查询的模式查询解码器，通过交叉注意力机制主动检索场景上下文信息，精确捕捉其未来轨迹的多模态分布；对数量较多且运动相对规则的非目标智能体，引入神经-贝塞尔解码器，通过神经网络预测贝塞尔曲线的控制点并结合数学表达式生成轨迹，在保证精度的同时显著降低推理延迟（latency），实现了高效的端到端训练与推理。在Argoverse与Argoverse 2数据集上的实验结果表明：相较于主流轻量化模型，本文模型以仅2.7 × 10

的参数量和18.6 ms的latency，取得了更高的预测精度，在精度、参数量与推理速度三者之间实现了最佳平衡，展现出优异的实际部署潜力。

Abstract

Accurate and efficient trajectory prediction is pivotal for safe navigation in autonomous driving systems. Existing methods typically employ graph neural networks (GNNs) or attention mechanisms to jointly model the spatio-temporal dependencies between agents and high-definition maps. Despite notable gains in accuracy

the coupled spatio-temporal modeling incurs excessive computational complexity

severely limiting deployment on embedded vehicular platforms. Moreover

prevailing decoders apply a uniform parameterized paradigm to all agents

overlooking the distinct requirements of different agents in terms of prediction accuracy and multimodality

which leads to inefficient resource allocation. To address these challenges

we propose a lightweight trajectory prediction model based on spatio-temporal dimension decomposition and heterogeneous decoding (MAM-M). In the encoding stage

we design a temporal dual-stream Mamba network that processes historical trajectories through parallel forward and reverse streams

enabling each time step to capture both past and future context. This effectively mitigates the long-range forgetting problem while maintaining linear computational complexity along the temporal dimension. A self-attention mechanism is then employed along the spatial dimension to capture global interactions between agents and map elements

realizing a “temporal-first

spatial-second” decoupled encoding strategy that significantly reduces redundant computations. In the decoding stage

we propose a heterogeneous decoder tailored to differen

t agents: for the target agent with complex motion and high behavioral uncertainty

a mode-query decoder with learnable queries retrieves scene context via cross-attention to accurately capture multimodal trajectory distributions; for the more numerous non-target agents with relatively regular motion

a neural-Bézier decoder predicts Bézier control points through a neural network and generates trajectories via mathematical formulation

substantially reducing inference latency while preserving accuracy and enabling efficient end-to-end training. Experiments on the Argoverse and Argoverse 2 datasets demonstrate that our model

with only 2.7×10

parameters and 18.6 ms inference latency

achieves superior prediction accuracy compared to mainstream lightweight models

attaining an optimal balance among accuracy

model size

and inference speed

and exhibiting excellent potential for practical deployment.

关键词

Keywords

references

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