metadata
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.