Vehicle Trajectory Prediction in Highway Merging Area Using Interactive Graph Attention Mechanism

Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism. Additionally, GAT are utilized to model the interactions between traffic agents and the map. The method we propose comprehensively incorporates the influences of time, space, and the map on trajectories. The interactive graph models can serve as effective prior knowledge for learning-based approaches, thereby enhancing the acquisition of interaction patterns among traffic scenarios and facilitating the interpretability of the method. We evaluate the performances of our method using real-world trajectory datasets from the highway merging area, i.e., the Exits and Entries Drone Dataset (exiD). Comparative analysis against classical algorithms demonstrates a reduced trajectory prediction error for prediction horizons of both 3s and 4s.