| tags: | |
| - autonomous-driving | |
| - motion-prediction | |
| - trajectory-prediction | |
| - pytorch | |
| - argoverse2 | |
| # Spline-Transformer Motion Predictor | |
| This repository contains the weights for a Transformer-based motion prediction model trained on the Argoverse 2 dataset. | |
| ## Model Architecture | |
| - **Base Architecture:** Transformer Encoder with Pre-Layer Normalization | |
| - **Output Representation:** 6 Bezier Spline Control Points | |
| - **Trajectory Generation:** Differentiable Bezier Spline Decoder outputs 30 future timesteps (3 seconds at 10Hz) | |
| - **Input Representation:** 20 past timesteps (2 seconds) of relative (x, y) coordinates | |
| - **Embedding Dimension:** 768 | |
| - **Attention Heads:** 8 | |
| - **Encoder Layers:** 5 | |
| ## Training Details | |
| - **Scale Factor:** 50.0 (Inputs and targets are divided by 50.0 before entering the model, and predictions are multiplied by 50.0 for real-world coordinate mapping). | |
| - **Loss Function:** Smooth Trajectory Loss (combining Huber Loss for ADE/FDE, a Continuity Anchor, and a Kinematic Smoothing Penalty). | |
| ## Usage | |
| To use these weights, initialize the `TransformerMotionPredictor` with the parameters listed above and load the `state_dict`. | |