针对复杂动态交互场景，智能车辆需准确感知当前交互风险，以保障行车安全。现有轨迹预测方法对车辆交互风险动态演化捕捉不足，缺乏对预测自一致性的考量。本文提出一种考虑动态交互风险的车辆连续轨迹预测方法。首先将车辆状态与交互关系编码为节点和边，构建图结构；随后将图结构序列输入交互风险感知编码器，利用风险场表征车辆交互风险，引入风险引导的图注意力机制与门控循环单元提取车辆时空交互风险特征；然后将包含交互风险特征的编码信息输入自一致预测注意力机制，利用历史预测信息校正当前嵌入特征；最后构建风险自适应连续解码器，生成考虑不确定性的多条可能轨迹及其分布概率。在HighD数据集上的实验表明，所提方法5s预测时域均方根误差为0.85m，对比最优基线模型，精度提升约16%；考虑车辆动态交互风险能够提高短时车辆轨迹预测精度；通过动态回溯历史预测结果，捕捉历史预测与当前预测的依赖关系能够提高长时车辆轨迹预测精度。

In complex multi-vehicle dynamic interaction scenarios, intelligent vehicles must accurately perceive current risks to ensure driving safety. Existing vehicle trajectory prediction methods fail largely to capture the dynamic evolution of vehicle interaction risks and lack considerations of prediction self-consistency. This study proposes a continuous vehicle trajectory prediction method that explicitly accounts for dynamic interaction risks. First, vehicle states and interaction relationships are encoded as nodes and edges, respectively, to construct a graph structure. This graph sequence is then fed into an interaction-risk-aware spatio-temporal encoder, where the risk field is employed to represent vehicle interaction risk. A risk-guided graph attention mechanism, combined with the gated recurrent units, are used to extract spatiotemporal interaction risk features. Subsequently, the encoded information containing interaction risk features is passed to a self-consistent prediction attention mechanism, which leverages historical prediction information to refine the current embedded features. Finally, the adjusted features are input into a risk-adaptive continuous decoder to generate multiple plausible trajectories along with their distribution probabilities, accounting for driving uncertainty. Experiments on the HighD dataset show that the proposed method achieves a root mean square error of 0.85 m over a 5-second prediction horizon, with a 16% improvement in accuracy compared to the best baseline model. The results demonstrate that incorporating dynamic interaction risk enhances short-term vehicle trajectory prediction accuracy, while dynamically revisiting historical predictions and capturing dependencies between past and current predictions improves long-term vehicle trajectory prediction performance.