Relation-based Motion Prediction using Traffic Scene Graphs

• URL: http://arxiv.org/abs/2212.02503v1
• Date: Thu, 24 Nov 2022 13:04:08 GMT
• Title: Relation-based Motion Prediction using Traffic Scene Graphs
• Authors: Maximilian Zipfl, Felix Hertlein, Achim Rettinger, Steffen Thoma, Lavdim Halilaj, Juergen Luettin, Stefan Schmid, Cory Henson
• Abstract summary: Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. In this work, we model traffic scenes in a form of spatial semantic scene graphs for various different predictions about the traffic participants. Our learning and inference approach uses Graph Neural Networks (GNNs) and shows that incorporating explicit information about the spatial semantic relations between traffic participants improves the predicdtion results.
• Score: 12.68339084600328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. Modeling the surrounding of an autonomous car using semantic relations, i.e., how different traffic participants relate in the context of traffic rule based behaviors, is hardly been considered in previous work. This stems from the fact that these relations are hard to extract from real-world traffic scenes. In this work, we model traffic scenes in a form of spatial semantic scene graphs for various different predictions about the traffic participants, e.g., acceleration and deceleration. Our learning and inference approach uses Graph Neural Networks (GNNs) and shows that incorporating explicit information about the spatial semantic relations between traffic participants improves the predicdtion results. Specifically, the acceleration prediction of traffic participants is improved by up to 12% compared to the baselines, which do not exploit this explicit information. Furthermore, by including additional information about previous scenes, we achieve 73% improvements.

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