Papers › CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural...
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention
Julian Schmidt, Julian Jordan, Franz Gritschneder, Klaus Dietmayer
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | DAC (K=6) | 0.9558 | #174 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | MR (K=1) | 0.6323 | #174 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | MR (K=6) | 0.2624 | #174 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | brier-minFDE (K=6) | 2.5926 | #174 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | minADE (K=1) | 1.8162 | #174 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | minADE (K=6) | 1.0626 | #174 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | minFDE (K=1) | 4.0576 | #174 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | CRAT-Pred | minFDE (K=6) | 1.8981 | #174 of 299 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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