Holistic Graph-based Motion Prediction

TL;DR

This paper introduces a novel graph-based motion prediction method for automated vehicles that integrates diverse information sources into a holistic heterogeneous graph, improving prediction accuracy in complex environments.

Contribution

The paper proposes a new heterogeneous graph representation combining temporal, relational, and static environment data for motion prediction in automated driving.

Findings

Effective on INTERACTION and Argoverse datasets

Ablation study shows the importance of different information types

Improved prediction accuracy over baseline methods

Abstract

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.