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Motion prediction for traffic participants is essential for a safe and robustautomated driving system, especially in cluttered urban environments. However,it is highly challenging due to the complex road topology as well as theuncertain intentions of the other agents. In this paper, we present agraph-based trajectory prediction network named the Dual Scale Predictor (DSP),which encodes both the static and dynamical driving context in a hierarchicalmanner. Different from methods based on a rasterized map or sparse lane graph,we consider the driving context as a graph with two layers, focusing on bothgeometrical and topological features. Graph neural networks (GNNs) are appliedto extract features with different levels of granularity, and features aresubsequently aggregated with attention-based inter-layer networks, realizingbetter local-global feature fusion. Following the recent goal-driven trajectoryprediction pipeline, goal candidates with high likelihood for the target agentare extracted, and predicted trajectories are generated conditioned on thesegoals. Thanks to the proposed dual-scale context fusion network, our DSP isable to generate accurate and human-like multi-modal trajectories. We evaluatethe proposed method on the large-scale Argoverse motion forecasting benchmark,and it achieves promising results, outperforming the recent state-of-the-artmethods.

Trajectory Prediction with Graph-based Dual-scale Context Fusion