mlsanityData to 7 Oct 2026

Holistic Graph-based Motion Prediction

arXiv 2301.13545 ↗First posted 31 Jan 2023cs.RO cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many factors influence the future motion of traffic participants starting with traffic rules and reaching from the interaction between each other to personal habits of human drivers. Therefore we present a novel approach for a graph-based prediction based on a heterogeneous holistic graph representation that combines temporal information, properties and relations between traffic participants as well as relations with static elements like the road network. The information are encoded through different types of nodes and edges that both are enriched with arbitrary features. We evaluated the approach on the INTERACTION and the Argoverse dataset and conducted an informative ablation study to demonstrate the benefit of different types of information for the motion prediction quality.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 31 of its 38 references that are in the map. First posted on arXiv 31 Jan 2023.

Builds on

31 of 38 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2017Graph Attention Networkscited by 8,854
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  8. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  9. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  10. 2021How Attentive are Graph Attention Networks?cited by 818
  11. 2019Heterogeneous Graph Attention Networkcited by 732
  12. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
19 more
  1. 2020Heterogeneous Graph Transformercited by 581
  2. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  3. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  4. 2020Learning Lane Graph Representations for Motion Forecastingcited by 380
  5. 2021AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecastingcited by 327
  6. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 292
  7. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  8. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  9. 2019PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settingscited by 178
  10. 2021Scene Transformer: A unified architecture for predicting multiple agent trajectoriescited by 165
  11. 2020A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sourcescited by 126
  12. 2019Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactionscited by 117
  13. 2022HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encodingcited by 109
  14. 2018Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Drivingcited by 90
  15. 2019Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecastingcited by 87
  16. 2021Spectral Temporal Graph Neural Network for Trajectory Predictioncited by 36
  17. 2021Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Drivingcited by 36
  18. 2020ReCoG: A Deep Learning Framework with Heterogeneous Graph for Interaction-Aware Trajectory Predictioncited by 20
  19. 2021Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Predictioncited by 16

Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 3; 2025: 2.

10 papers.

  1. 2024SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphscited by 10
  2. 2023RedMotion: Motion Prediction via Redundancy Reductioncited by 6
  3. 2023nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory predictioncited by 6
  4. 2023Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Drivingcited by 5
  5. 2023KI-PMF: Knowledge Integrated Plausible Motion Forecastingcited by 5
  6. 2024SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Predictioncited by 5
  7. 2023Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactionscited by 4
  8. 2024MAP-Former: Multi-Agent-Pair Gaussian Joint Predictioncited by 2
  9. 2025Goal-based Trajectory Prediction for improved Cross-Dataset Generalizationcited by 2
  10. 2025Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Drivingcited by 0