摘要：

现有的轨迹预测工作大多依赖于高精地图, 但高精地图的采集耗时长、成本高、处理复杂, 难以快速适应智能交通的大面积普及。为解决无地图场景下车辆轨迹预测问题, 提出一种基于多模态数据时空特征的轨迹预测方法。构建多个历史轨迹时空交互图, 交叉使用时间和空间注意力并进行深度融合, 以建模道路上车辆之间的时空关联性。在此基础上, 利用残差网络进行多目标多模态轨迹生成。在真实数据集Argoverse 2上进行模型的训练和测试, 实验结果表明, 相较于CRAT-Pred方法, 该模型在单模态预测方面最小平均位移误差、最小最终位移误差和未命中率指标分别提升了3.86%、3.89%、0.48%, 在多模态预测方面各项指标分别提升了0.78%、0.96%、0.42%。该方法能够有效地捕捉车辆移动轨迹的时间和空间特征, 并可在自动驾驶等相关领域得到有效应用。

关键词: 多模态任务, 轨迹预测, 时空特征, 注意力机制, 交叉注意力

Abstract:

Existing trajectory prediction methods rely heavily on high-definition maps, which are time-consuming, costly, and complex to acquire. This makes it difficult for them to quickly adapt to the widespread adoption of intelligent transportation. To address the problem of vehicle trajectory prediction in map-free scenes, a trajectory prediction method based on spatio-temporal features of multi-modal data is proposed in this paper. Multiple spatio-temporal interaction graphs are constructed from the history of the trajectory, temporal and spatial attention are cross-utilized and deeply fused to model the spatio-temporal correlations between vehicles on the road. Finally, a residual network is used for a multi-objective and multi-modal trajectory generation. The model is trained and tested on the real dataset, Argoverse 2, and the experimental results show that compared with the CRAT-Pred, this model can improve minADE, minFDE and Miss Rate(MR) metrics in single-modal prediction by 3.86%, 3.89%, and 0.48%, and in multi-modal prediction by 0.78%, 0.96% and 0.42%. Hence, the proposed trajectory prediction method can efficiently capture the temporal and spatial characteristics of vehicle movement trajectories and can be effectively applied in related fields such as autonomous driving.

Key words: multi-modal task, trajectory prediction, spatio-temporal feature, attention mechanism, crossattention