mlsanityData to 7 Oct 2026

SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs

arXiv 2102.06361 ↗First posted 12 Feb 2021cs.LG cs.AIcited by 16

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Abstract (arXiv)

Autonomous vehicles navigate in dynamically changing environments under a wide variety of conditions, being continuously influenced by surrounding objects. Modelling interactions among agents is essential for accurately forecasting other agents' behaviour and achieving safe and comfortable motion planning. In this work, we propose SCOUT, a novel Attention-based Graph Neural Network that uses a flexible and generic representation of the scene as a graph for modelling interactions, and predicts socially-consistent trajectories of vehicles and Vulnerable Road Users (VRUs) under mixed traffic conditions. We explore three different attention mechanisms and test our scheme with both bird-eye-view and on-vehicle urban data, achieving superior performance than existing state-of-the-art approaches on InD and ApolloScape Trajectory benchmarks. Additionally, we evaluate our model's flexibility and transferability by testing it under completely new scenarios on RounD dataset. The importance and influence of each interaction in the final prediction is explored by means of Integrated Gradients technique and the visualization of the attention learned.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

17 of 25 references.

References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  4. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  5. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  6. 2018Neural Relational Inference for Interacting Systemscited by 339
  7. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  8. 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
  9. 2018TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agentscited by 111
  10. 2019Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Datacited by 104
  11. 2018TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactionscited by 82
  12. 2019GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Drivingcited by 73
5 more
  1. 2019Graph Neural Networks for Modelling Traffic Participant Interactioncited by 42
  2. 2019StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topologycited by 17
  3. 2020AMENet: Attentive Maps Encoder Network for Trajectory Predictioncited by 17
  4. 2020Exploring Dynamic Context for Multi-path Trajectory Predictioncited by 14
  5. 2020Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNscited by 4

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Cited by year

Citing papers per year: 2021: 1; 2022: 5; 2023: 4; 2024: 5; 2025: 1.

16 papers.

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  1. 2023Leveraging Future Relationship Reasoning for Vehicle Trajectory Predictioncited by 47
  2. 2023Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Reviewcited by 18
  3. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  4. 2022Towards Explainable Motion Prediction using Heterogeneous Graph Representationscited by 10
  5. 2023EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Predictioncited by 10
  6. 2022Aware of the History: Trajectory Forecasting with the Local Behavior Datacited by 8
  7. 2022Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Predictioncited by 7
  8. 2024Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmapcited by 6
  9. 2022Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputscited by 4
  10. 2024Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairnesscited by 3
  11. 2023Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checkscited by 2
  12. 2024Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasetscited by 2
4 more
  1. 2024Remember and Recall: Associative-Memory-based Trajectory Predictioncited by 2
  2. 2024Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trendscited by 1
  3. 2021Jointly Learning Agent and Lane Information for Multimodal Trajectory Predictioncited by 0
  4. 2022Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecastscited by 0