Abstract: With the rapid development of autonomous driving technology, accurate trajectory prediction has become essential for ensuring driving safety. In this context, Adversarial Multimodal LSTM–Informer for Integrated Driving Intention and Trajectory Prediction (AMLI-DIR) is proposed. The model adopts a hierarchical architecture. In the intention recognition layer, a GATv2-BiLSTM network is constructed to extract the spatial and temporal features of the target vehicle and its surrounding vehicles. Meanwhile, a spatiotemporal cross-attention mechanism is introduced to effectively fuse these features, thereby achieving precise driving intention recognition. In the trajectory prediction layer, independent prediction models are built for lane-keeping and lane-changing scenarios, and a multi-criteria generator is employed to produce accurate predicted trajectories. During the prediction stage, the AMLI-DIR model first identifies the most probable driving intention and then activates the corresponding trajectory prediction model, enabling intention-specific trajectory prediction. The model is trained, validated, and tested using the NGSIM and CQSkyEyeX datasets based on real-world traffic scenarios. Experimental results demonstrate that the AMLI-DIR model outperforms all comparison models across multiple evaluation metrics. Notably, in long-term prediction (3 s), it achieves the lowest RMSE of 1.05 m, which is approximately 22.2% lower than that of the second-best model, STEI. Furthermore, the RMSE of the AMLI-DIR model increases by only 0.26 m from 1 s to 3 s, significantly lower than other models, further validating its effectiveness and superiority in trajectory prediction tasks.

摘要： 随着自动驾驶技术的快速发展，精确的轨迹预测已成为安全驾驶的关键。鉴于此，提出一种基于LSTM-Informer多模态对抗学习的车辆驾驶意图与轨迹融合预测模型（AMLI-DIR）。该模型采用分层架构，在意图识别层，通过构建GATv2-BiLSTM网络提取目标车与周围车的空间及时序特征，并引入时空交叉注意力机制融合时空特征，从而实现驾驶意图的精准识别。在轨迹预测层，针对直行与换道场景分别构建独立的轨迹预测模型，同时利用多准则生成器生成精准的预测轨迹。在预测阶段，ALMI-DIR模型首先筛选出概率最大的意图类型，随后调用对应意图的轨迹预测模型，实现针对不同意图的精准轨迹预测。使用基于真实路况信息的NGSIM与CQSkyEyeX数据集对模型进行训练、验证与测试。实验结果表明，AMLI-DIR模型在各项评估指标上均优于其它对比模型，尤其是在长时预测阶段（3s）其均方根误差最低，仅为1.05m，较表现次优的STEI模型降低约22.2%。此外该模型的RMSE从1s到3s仅增加0.26m，误差增长率远低于其它模型，进一步验证了模型在轨迹预测任务中的有效性与优越性。