Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning

Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints. In this work, we propose a risk a...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-11, Vol.24 (11), p.1-14
Main Authors: Fang, Jianwu, Zhu, Chen, Zhang, Pu, Yu, Hongkai, Xue, Jianru
Format: Article
Language:English
Subjects:
collision risk
Forecasting
Heterogeneous trajectory forecasting
Intelligent transportation systems
intelligent vehicles
Layout
Learning
Mathematical models
MLP
Pedestrians
Predictive models
Risk
road scene graph
Roads
Semantics
Trajectory
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Summary:Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints. In this work, we propose a risk and scene graph learning method for trajectory forecasting of heterogeneous road agents, which consists of a Heterogeneous Risk Graph (HRG) and a Hierarchical Scene Graph (HSG) from the aspects of agent category and their movable semantic regions. HRG groups each kind of road agent and calculates their interaction adjacency matrix based on an effective collision risk metric. HSG of the driving scene is modeled by inferring the relationship between road agents and road semantic layout aligned by the road scene grammar. Based on this formulation, we can obtain effective trajectory forecasting in driving situations, and comparable performance to other state-of-the-art approaches is presented by extensive experiments on the nuScenes, ApolloScape, and Argoverse datasets.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3287186