mlsanityData to 7 Oct 2026

Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning

arXiv 2409.11676 ↗First posted 18 Sep 2024cs.RO cs.AI cs.LGcited by 6

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Abstract (arXiv)

The intricate nature of real-world driving environments, characterized by dynamic and diverse interactions among multiple vehicles and their possible future states, presents considerable challenges in accurately predicting the motion states of vehicles and handling the uncertainty inherent in the predictions. Addressing these challenges requires comprehensive modeling and reasoning to capture the implicit relations among vehicles and the corresponding diverse behaviors. This research introduces an integrated framework for autonomous vehicles (AVs) motion prediction to address these complexities, utilizing a novel Relational Hypergraph Interaction-informed Neural mOtion generator (RHINO). RHINO leverages hypergraph-based relational reasoning by integrating a multi-scale hypergraph neural network to model group-wise interactions among multiple vehicles and their multi-modal driving behaviors, thereby enhancing motion prediction accuracy and reliability. Experimental validation using real-world datasets demonstrates the superior performance of this framework in improving predictive accuracy and fostering socially aware automated driving in dynamic traffic scenarios. The source code is publicly available at https://github.com/keshuw95/RHINO-Hypergraph-Motion-Generation.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 72 references that are in the map. First posted on arXiv 18 Sep 2024.

Builds on

32 of 72 references.

References · 32

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
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  3. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
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  9. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  10. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  11. 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
  12. 2023GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Drivingcited by 141
20 more
  1. 2022GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoningcited by 128
  2. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 90
  3. 2020PiP: Planning-informed Trajectory Prediction for Autonomous Drivingcited by 80
  4. 2020What-If Motion Prediction for Autonomous Drivingcited by 80
  5. 2019Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANscited by 78
  6. 2018Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMscited by 77
  7. 2019GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Drivingcited by 73
  8. 2023Leveraging Future Relationship Reasoning for Vehicle Trajectory Predictioncited by 47
  9. 2020Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learningcited by 46
  10. 2021Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Drivingcited by 36
  11. 2022Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoningcited by 28
  12. 2018Conditional Variational Autoencoder for Neural Machine Translationcited by 20
  13. 2024Multi-Agent Dynamic Relational Reasoning for Social Robot Navigationcited by 20
  14. 2021Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecastingcited by 19
  15. 2024Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generationcited by 18
  16. 2024Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environmentscited by 13
  17. 2020Multi-Head Attention based Probabilistic Vehicle Trajectory Predictioncited by 12
  18. 2023EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Predictioncited by 10
  19. 2023Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networkscited by 10
  20. 2023Collective Relational Inference for learning heterogeneous interactionscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
  2. 2025AI2-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamicscited by 6
  3. 2025Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happencited by 5
  4. 2025Automated Vehicles Should be Connected with Natural Languagecited by 5
  5. 2026Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Modelscited by 2
  6. 2026Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinementcited by 1