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2021

Trajectory Prediction with Graph-based Dual-scale Context Fusion

Zhang, Lu, Li, Peiliang, Chen, Jing et al.

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Motion prediction for traffic participants is essential for a safe and robust automated driving system, especially in cluttered urban environments.

However, it is highly challenging due to the complex road topology as well as the uncertain intentions of the other agents.
In this paper, we present a graph-based trajectory prediction network named the Dual Scale Predictor (DSP), which encodes both the static and dynamical driving context in a hierarchical manner.
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 both geometrical and topological features.

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