Path-Aware Graph Attention for HD Maps in Motion Prediction
- URL: http://arxiv.org/abs/2202.13772v1
- Date: Wed, 23 Feb 2022 09:43:47 GMT
- Title: Path-Aware Graph Attention for HD Maps in Motion Prediction
- Authors: Fang Da and Yu Zhang
- Abstract summary: Success of motion prediction for autonomous driving relies on integration of information from the HD maps.
We propose Path-Aware Graph Attention, a novel attention architecture that infers the attention between two vertices by parsing the sequence of edges forming the paths that connect them.
Our analysis illustrates how the proposed attention mechanism can facilitate learning in a didactic problem where existing graph networks like GCN struggle.
- Score: 4.531240717484252
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The success of motion prediction for autonomous driving relies on integration
of information from the HD maps. As maps are naturally graph-structured,
investigation on graph neural networks (GNNs) for encoding HD maps is
burgeoning in recent years. However, unlike many other applications where GNNs
have been straightforwardly deployed, HD maps are heterogeneous graphs where
vertices (lanes) are connected by edges (lane-lane interaction relationships)
of various nature, and most graph-based models are not designed to understand
the variety of edge types which provide crucial cues for predicting how the
agents would travel the lanes. To overcome this challenge, we propose
Path-Aware Graph Attention, a novel attention architecture that infers the
attention between two vertices by parsing the sequence of edges forming the
paths that connect them. Our analysis illustrates how the proposed attention
mechanism can facilitate learning in a didactic problem where existing graph
networks like GCN struggle. By improving map encoding, the proposed model
surpasses previous state of the art on the Argoverse Motion Forecasting
dataset, and won the first place in the 2021 Argoverse Motion Forecasting
Competition.
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