Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks

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dc.contributor.authorMo, Xiaoyu
dc.contributor.authorXing, Yang
dc.contributor.authorLiu, Haochen
dc.contributor.authorLv, Chen
dc.date.accessioned2023-06-21T14:18:41Z
dc.date.available2023-06-21T14:18:41Z
dc.date.issued2023-04-26
dc.description.abstractPredicting the multimodal future motions of neighboring agents is essential for an autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an agent is affected by the complex interaction among itself, other agents, and the local roads. Unlike most existing works, which predict a fixed number of possible future motions of an agent, we propose a map-adaptive predictor that can predict a variable number of future trajectories of an agent according to the number of lanes with candidate centerlines (CCLs). The predictor predicts not only future motions guided by single CCLs but also a scene-reasoning prediction and a motion-maintaining prediction. These three kinds of predictions are produced integrally via a single graph operation. We represent the driving scene with a heterogeneous hierarchical graph containing nodes of two types. An agent node contains its dynamics feature encoded from its historical states, and a CCL node contains the CCL's sequential feature. We propose a hierarchical graph operator (HGO) with an edge-masking technology to regulate the information flow in graph operations and obtain the encoded scene feature for the trajectory decoder. Experiments on two large-scale real-world driving datasets show that our method realizes map-adaptive prediction and outperforms strong baselines.en_UK
dc.identifier.citationMo X, Xing Y, Liu H, Lv C. (2023) Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks. IEEE Robotics and Automation Letters, Volume 8, Issue 6, June 2023, pp. 3685-3692en_UK
dc.identifier.issn2377-3766
dc.identifier.urihttps://doi.org/10.1109/LRA.2023.3270739
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19830
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMap-adaptive trajectory predictionen_UK
dc.subjectconnected vehiclesen_UK
dc.subjectgraph neural networksen_UK
dc.subjectheterogeneous interactionsen_UK
dc.titleMap-adaptive multimodal trajectory prediction using hierarchical graph neural networksen_UK
dc.typeArticleen_UK

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