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

arXiv: https://arxiv.org/abs/2202.04488 · HTML: https://mlsanity.com/paper/2202.04488/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [Multiple Futures Prediction](https://mlsanity.com/paper/1911.00997/) (2019)
- [Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions](https://mlsanity.com/paper/1906.08945/) (2019)
- [Implicit Latent Variable Model for Scene-Consistent Motion Forecasting](https://mlsanity.com/paper/2007.12036/) (2020)
- [LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting](https://mlsanity.com/paper/2101.06653/) (2021)
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://mlsanity.com/paper/1811.02146/) (2018)
- [TPCN: Temporal Point Cloud Networks for Motion Forecasting](https://mlsanity.com/paper/2103.03067/) (2021)
- [Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving](https://mlsanity.com/paper/1808.05819/) (2018)
- [Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting](https://mlsanity.com/paper/1910.03650/) (2019)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [What-If Motion Prediction for Autonomous Driving](https://mlsanity.com/paper/2008.10587/) (2020)
- [LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents](https://mlsanity.com/paper/2104.00249/) (2021)
- [Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network](https://mlsanity.com/paper/2002.06241/) (2020)

## Built on by (most cited first, 21 of 21)

- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2301.03512/) (2023)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios](https://mlsanity.com/paper/2210.02300/) (2022)
- [GenAI-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 Systems](https://mlsanity.com/paper/2409.00494/) (2024)
- [Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios](https://mlsanity.com/paper/2306.05478/) (2023)
- [Exploring Navigation Maps for Learning-Based Motion Prediction](https://mlsanity.com/paper/2302.06195/) (2023)
- [KI-PMF: Knowledge Integrated Plausible Motion Forecasting](https://mlsanity.com/paper/2310.12007/) (2023)
- [RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction](https://mlsanity.com/paper/2304.05856/) (2023)
- [MEAT: Maneuver Extraction from Agent Trajectories](https://mlsanity.com/paper/2206.05158/) (2022)
- [Enhancing Mapless Trajectory Prediction through Knowledge Distillation](https://mlsanity.com/paper/2306.14177/) (2023)
- [Efficient Baselines for Motion Prediction in Autonomous Driving](https://mlsanity.com/paper/2309.03387/) (2023)
- [FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2411.16747/) (2024)
- [A Fast and Map-Free Model for Trajectory Prediction in Traffics](https://mlsanity.com/paper/2307.09831/) (2023)
- [Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction](https://mlsanity.com/paper/2411.06851/) (2024)
- [LMR: Lane Distance-Based Metric for Trajectory Prediction](https://mlsanity.com/paper/2304.05869/) (2023)
- [Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment](https://mlsanity.com/paper/2407.08932/) (2024)
- [Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encoding](https://mlsanity.com/paper/2411.10961/) (2024)
- [BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving](https://mlsanity.com/paper/2604.02930/) (2026)
- [BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposals](https://mlsanity.com/paper/2509.10080/) (2025)
- [Integrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps](https://mlsanity.com/paper/2602.07938/) (2026)

## 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.