Multi-modal vehicle trajectory prediction via hierarchical attention and raster-vector maps encoding in unstructured road environments

Trajectory prediction for autonomous vehicles in open-pit mines is challenging due to unstructured roads, the absence of lane markings, and complex vehicle interactions. In terms of AI methodology, the core contribution of this paper is a novel hybrid raster-vector map fusion network, designed to address these challenges. The framework encodes both drivable area raster features and sparse lane graph vectors, followed by a hierarchical attention mechanism for cross-modality map fusion, agent-to-agent interaction, and map-to-agent contextualization. For the engineering application, we focus on deploying and validating this model in real-world open-pit mining operations. Evaluated on a dedicated dataset, our method achieves a minimum Final Displacement Error (minFDE) of 1.71 m for a 6-s prediction horizon, reducing the miss rate by 36.4 % compared to a strong raster-based baseline. Real-world validation over 4 h of operation in a Chinese open-pit mine, involving over 30 interaction scenarios, confirms the framework's practical viability for autonomous haul trucks.