Adaptive Multi-Agent Trajectory Prediction with Hierarchical Graph-Based Environment Fusion

Accurate trajectory prediction for all agents within complex environments is a crucial step toward realizing autonomous driving navigation. However, this task poses significant challenges due to the uncertainty surrounding the agent's intentions and the intricate road topology. Existing trajectory prediction methods struggle to strike a balance between accuracy and efficiency. To address this challenge, we propose the graph-based trajectory prediction network (DGATP). The model utilizes a two-layer graph representation to capture both the geometric and topological features of the driving environment information and encodes the static and dynamic driving environments hierarchically. An inter-layer network employing an attention mechanism is employed for feature aggregation, leading to improved local-global feature fusion. Furthermore, we introduce a joint prediction framework for all agents in the scenario, which utilizes dynamic weight learning. This adaptive head enhances the model's capacity without increasing its size, thereby maintaining the efficiency of the inference process and leading to accurate and efficient trajectory predictions.