Abstract: To address the issues of insufficient feature interaction depth and weak long-term sequence modeling capability in existing trajectory prediction models, a vehicle trajectory prediction model based on coarse and fine-grained feature interaction and long short-term memory enhancement is proposed. This model aims to achieve interactive enhancement of coarse and fine-grained features in the scene, deeply integrating the inherent advantages of dual perspectives. It extracts coarse-grained features such as road structure and traffic flow distribution from the scene center perspective to construct a macroscopic motion framework; and extracts fine-grained features such as the relative motion between the target vehicle and surrounding agents and local interaction relationships from the agent center perspective to depict microscopic behavior details. Through the dynamic constraint and deep interaction of fine-grained features on coarse-grained features, the problem of insufficient feature interaction depth is effectively improved, achieving precise refinement of the end positions of multi-modal predicted trajectories. Meanwhile, to effectively alleviate the weakness in long-term sequence modeling capability, a long short-term memory enhancement module with dual memory units is designed to capture long-distance temporal dependency features. Through feature weighting and trajectory endpoint correction strategies, the model's prediction capability for long-term trajectories is effectively enhanced. Experimental results show that compared with mainstream trajectory prediction models, the proposed method has significant improvements in key indicators. On the Argoverse 1 dataset, the average improvements in the minimum final displacement error, minimum average displacement error, and minimum final displacement error are 4.4%, 5.4%, and 4.9% respectively. On the Argoverse 2 dataset, the corresponding indicators are improved by an average of 5.1%, 6.3%, and 5.8% respectively. This result not only proves the improvement in trajectory prediction accuracy of the proposed model but also verifies its generalization effectiveness in different data distribution scenarios.

摘要： 针对现有轨迹预测模型特征交互深度不足以及长时序建模能力薄弱的问题，提出了一种基于粗细粒度特征交互与长短期记忆增强的车辆轨迹预测模型。该模型以实现场景粗粒度与细粒度特征的交互式增强为目标，深度整合了双重视角固有优势，从场景中心视角提取道路结构、车流分布等粗粒度特征，构建宏观运动框架；从智能体中心视角提取目标车辆与周边智能体的相对运动、局部交互关系等细粒度特征，刻画微观行为细节。通过细粒度特征对粗粒度特征的动态约束与深度交互，有效改善特征交互深度不足的问题，实现多模态预测轨迹端点位置的精准细化。同时，为有效缓解长时序建模能力薄弱的问题，设计了含双记忆单元的长短期记忆增强模块，以捕捉长距离时序依赖特征，并通过特征加权与轨迹端点修正策略，有效增强模型对长时序轨迹的预测能力。实验结果表明，相较于主流轨迹预测模型，所提方法在关键指标上均有显著提升，在Argoverse 1数据集上，概率最小最终位移误差、最小最终位移误差和最小平均位移误差指标分别平均提升4.4%、5.4%、4.9%，在Argoverse 2数据集上，对应指标分别平均提升5.1%、6.3%、5.8%。这一结果不仅证明了所提模型在轨迹预测准确性上的提升，更验证了其在不同数据分布场景下的泛化有效性。