Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as well as interactions with surrounding vehicles. These intricate interactions arise from unpredictable motion patterns, leading to a wide range of driving behaviors that warrant in-depth investigation. This study presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction (GIMTP) framework, designed to probabilistically predict future vehicle trajectories by effectively capturing these interactions. Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix. To holistically capture both spatial and temporal dependencies embedded in this dynamic adjacency matrix, the methodology incorporates the Diffusion Graph Convolutional Network (DGCN), thereby providing a graph embedding of both historical states and future states. Furthermore, we employ a driving intention-specific feature fusion, enabling the adaptive integration of historical and future embeddings for enhanced intention recognition and trajectory prediction. This model gives two-dimensional predictions for each mode of longitudinal and lateral driving behaviors and offers probabilistic future paths with corresponding probabilities, addressing the challenges of complex vehicle interactions and multi-modality of driving behaviors. Validation using real-world trajectory datasets demonstrates the efficiency and potential.

翻译：预测车辆轨迹对于确保自动驾驶车辆的运行效率和安全性至关重要，尤其是在拥堵的多车道高速公路上。在此类动态环境中，车辆的运动由其历史行为以及与周围车辆的交互共同决定。这些复杂的交互源于不可预测的运动模式，导致多种驾驶行为值得深入研究。本研究提出了基于图交互感知的多模态轨迹预测（GIMTP）框架，旨在通过有效捕捉这些交互来概率性地预测未来车辆轨迹。在该框架中，车辆运动被概念化为时变图上的节点，交通交互则通过动态邻接矩阵表示。为全面捕捉该动态邻接矩阵中蕴含的空间和时间依赖性，该方法融合了扩散图卷积网络（DGCN），从而提供历史状态和未来状态的图嵌入。此外，我们采用驾驶意图特定特征融合技术，实现历史和未来嵌入的自适应集成，以增强意图识别和轨迹预测。该模型为每种纵向和横向驾驶行为模式提供二维预测，并给出对应概率的未来路径，从而应对复杂车辆交互和驾驶行为多模态性的挑战。基于真实世界轨迹数据集的验证证明了该方法的有效性和潜力。