Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction

Sur, Lingfeng; Tang, Chen; Niu, Yaru; Sachdeva, Enna; Choi, Chiho; Misu, Teruhisa; Tomizuka, Masayoshi; Zhan, Wei

doi:10.1109/iros47612.2022.9982147

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…Agent-Agent,1 Rel: Relation type, 2 EA: Edge Attribute,3 HoN: Homogeneous Node,4 HoE: Homogeneous Edge,5 HeN: Heterogeneous Node,6 HeE: Heterogeneous Edge,…”

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confidence: 99%

A novel method for drug-target interaction prediction based on graph transformers model

Wang

Hong-mei

Guo

et al. 2022

Preprint

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Background:drug-target interactions prediction(DTIs) becomes more and moreimportant for accelerating drug research and drug repositioning. drug-targetinteraction network is a typical model for DTIs prediction. As many differenttypes of relationships exist between drug and target, drug-target interactionnetwork can be used for modeling drug-target interaction relationship. Recentworks on drug-target interaction network are mostly concentrate on drug node ortarget node and neglecting the relationships between drug-target. Results:We propose a novel prediction method for modeling the relationshipbetween drug and target independently. Firstly, we use different level relationshipsof drugs and targets to construct feature of drug-target interaction. Then, we useline graph to model drug-target interaction. After that, we introduce graphtransformer network to predict drug-target interaction. Conclusions:We introduce line graph to model the relationship between drug andtarget. After transformed drug-target interaction from links into nodes, we usegraph transformer network to fulfill drug-target interaction prediction task. Keywords: drug-target interaction; graph attention network; line graph

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“…Agent-Agent,1 Rel: Relation type, 2 EA: Edge Attribute,3 HoN: Homogeneous Node,4 HoE: Homogeneous Edge,5 HeN: Heterogeneous Node,6 HeE: Heterogeneous Edge,…”

mentioning

confidence: 99%

A novel method for drug-target interaction prediction based on graph transformers model

Wang

Hong-mei

Guo

et al. 2022

Preprint

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SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction

Bhattacharyya,

Huang,

Czarnecki

2024

2024 IEEE Intelligent Vehicles Symposium (IV)

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Efficient Speed Planning for Autonomous Driving in Dynamic Environment With Interaction Point Model

Chen

Xin

Cheng

et al. 2022

IEEE Robot. Autom. Lett.

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Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are designed for particular scenarios and require significant human labor in parameter tuning to be applied to different situations. To solve this problem, we first propose a learning-based Interaction Point Model (IPM), which describes the interaction between agents with the protection time and interaction priority in a unified manner. We further integrate the proposed IPM into a novel planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.

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Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction

Cited by 7 publications

References 16 publications

A novel method for drug-target interaction prediction based on graph transformers model

A novel method for drug-target interaction prediction based on graph transformers model

SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction

Efficient Speed Planning for Autonomous Driving in Dynamic Environment With Interaction Point Model

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