---
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`.