Holistic Graph-based Motion Prediction
This addresses the problem of predicting traffic participant motion for automated vehicles, which is incremental as it builds on existing graph-based methods with a more comprehensive representation.
The paper tackles motion prediction for automated vehicles in complex environments by introducing a graph-based approach using a heterogeneous holistic graph representation, achieving improved prediction quality as demonstrated through evaluation on the INTERACTION and Argoverse datasets with an ablation study.
Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many factors influence the future motion of traffic participants starting with traffic rules and reaching from the interaction between each other to personal habits of human drivers. Therefore we present a novel approach for a graph-based prediction based on a heterogeneous holistic graph representation that combines temporal information, properties and relations between traffic participants as well as relations with static elements like the road network. The information are encoded through different types of nodes and edges that both are enriched with arbitrary features. We evaluated the approach on the INTERACTION and the Argoverse dataset and conducted an informative ablation study to demonstrate the benefit of different types of information for the motion prediction quality.