IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction

Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneously processing multiple agents lies in modeling complex social interactions caused by various driving intentions and road...

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Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Zhu, Dekai, Zhai, Guangyao, Yan, Di, Manhardt, Fabian, Berkemeyer, Hendrik, Tran, Tuan, Navab, Nassir, Tombari, Federico, Busam, Benjamin
Format: Article
Language:English
Subjects:
Correlation coefficients
Decoders
Interaction models
Modelling
Modules
Multiagent systems
Road conditions
Social factors
Online Access:Get full text
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Summary:Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneously processing multiple agents lies in modeling complex social interactions caused by various driving intentions and road conditions. Previous methods typically leverage graph-based message propagation or attention mechanism to encapsulate such interactions in the format of marginal probabilistic distributions. However, it is inherently sub-optimal. In this paper, we propose IPCC-TP, a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. IPCC-TP learns pairwise joint Gaussian Distributions through the tightly-coupled estimation of the means and covariances according to interactive incremental movements. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders. Extensive experiments on nuScenes and Argoverse 2 datasets demonstrate that IPCC-TP improves the performance of baselines by a large margin.
ISSN:2331-8422