mlsanityData to 7 Oct 2026

CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention

arXiv 2202.04488 ↗First posted 9 Feb 2022cs.CV cs.LG cs.ROcited by 21

Not on the map. Why?

Abstract (arXiv)

Predicting the motion of surrounding vehicles is essential for autonomous vehicles, as it governs their own motion plan. Current state-of-the-art vehicle prediction models heavily rely on map information. In reality, however, this information is not always available. We therefore propose CRAT-Pred, a multi-modal and non-rasterization-based trajectory prediction model, specifically designed to effectively model social interactions between vehicles, without relying on map information. CRAT-Pred applies a graph convolution method originating from the field of material science to vehicle prediction, allowing to efficiently leverage edge features, and combines it with multi-head self-attention. Compared to other map-free approaches, the model achieves state-of-the-art performance with a significantly lower number of model parameters. In addition to that, we quantitatively show that the self-attention mechanism is able to learn social interactions between vehicles, with the weights representing a measurable interaction score. The source code is publicly available.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 9 Feb 2022.

Builds on

23 of 30 references.

References · 23

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11 more
  1. 2019Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactionscited by 117
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 10; 2024: 5; 2025: 2; 2026: 2.

21 papers.

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  1. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  2. 2023SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networkscited by 18
  3. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  4. 2022Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarioscited by 12
  5. 2024GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systemscited by 9
  6. 2023Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarioscited by 6
  7. 2023Exploring Navigation Maps for Learning-Based Motion Predictioncited by 5
  8. 2023KI-PMF: Knowledge Integrated Plausible Motion Forecastingcited by 5
  9. 2023RESET: Revisiting Trajectory Sets for Conditional Behavior Predictioncited by 4
  10. 2022MEAT: Maneuver Extraction from Agent Trajectoriescited by 3
  11. 2023Enhancing Mapless Trajectory Prediction through Knowledge Distillationcited by 3
  12. 2023Efficient Baselines for Motion Prediction in Autonomous Drivingcited by 3
9 more
  1. 2024FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Predictioncited by 3
  2. 2023A Fast and Map-Free Model for Trajectory Prediction in Trafficscited by 2
  3. 2024Fast and Efficient Transformer-based Method for Bird's Eye View Instance Predictioncited by 2
  4. 2023LMR: Lane Distance-Based Metric for Trajectory Predictioncited by 1
  5. 2024Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environmentcited by 1
  6. 2024Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encodingcited by 1
  7. 2026BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Drivingcited by 1
  8. 2025BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposalscited by 0
  9. 2026Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Mapscited by 0