Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network

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dc.contributor.authorMo, Xiaoyu
dc.contributor.authorHuang, Zhiyu
dc.contributor.authorXing, Yang
dc.contributor.authorLv, Chen
dc.date.accessioned2022-02-03T19:39:51Z
dc.date.available2022-02-03T19:39:51Z
dc.date.issued2022-02-01
dc.description.abstractSimultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations. Two main challenges for this task are to handle the varying number of heterogeneous target agents and jointly consider multiple factors that would affect their future motions. This is because different kinds of agents have different motion patterns, and their behaviors are jointly affected by their individual dynamics, their interactions with surrounding agents, as well as the traffic infrastructures. A trajectory prediction method handling these challenges will benefit the downstream decision-making and planning modules of autonomous vehicles. To meet these challenges, we propose a three-channel framework together with a novel Heterogeneous Edge-enhanced graph ATtention network (HEAT). Our framework is able to deal with the heterogeneity of the target agents and traffic participants involved. Specifically, agents' dynamics are extracted from their historical states using type-specific encoders. The inter-agent interactions are represented with a directed edge-featured heterogeneous graph and processed by the designed HEAT network to extract interaction features. Besides, the map features are shared across all agents by introducing a selective gate-mechanism. And finally, the trajectories of multiple agents are predicted simultaneously. Validations using both urban and highway driving datasets show that the proposed model can realize simultaneous trajectory predictions for multiple agents under complex traffic situations, and achieve state-of-the-art performance with respect to prediction accuracy. The achieved final displacement error (FDE@3sec) is 0.66 meter under urban driving, demonstrating the feasibility and effectiveness of the proposed approach.en_UK
dc.identifier.citationMo X, Huang Z, Xing Y, Lv C. (2022) Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network. IEEE Transactions on Intelligent Transportation Systems, Volume 23, Number 7, July 2022, pp. 9554-9567en_UK
dc.identifier.issn1524-9050
dc.identifier.urihttps://doi.org/10.1109/TITS.2022.3146300
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17541
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectTrajectory predictionen_UK
dc.subjectconnected vehiclesen_UK
dc.subjectgraph neural networksen_UK
dc.subjectheterogeneous interactionsen_UK
dc.titleMulti-agent trajectory prediction with heterogeneous edge-enhanced graph attention networken_UK
dc.typeArticleen_UK

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