standard-e2e 0.0.5

A unified framework for autonomous driving dataset management for end-to-end model development

A framework for unified end-to-end autonomous driving datasets processing

StandardE2E provides a consistent interface for preprocessing, loading, and training with multimodal data from various end-to-end autonomous driving datasets. It standardizes the complex process of working with different dataset formats, allowing researchers to focus on model development rather than data engineering.

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📖 Documentation

• Latest docs: https://standarde2e.readthedocs.io/en/latest/

📦 Installation

Option 1: From PyPI (Recommended for Users)

pip install standard-e2e

Option 2: Development with uv (recommended)

# Install uv: https://docs.astral.sh/uv/
git clone https://github.com/stepankonev/StandardE2E.git
cd StandardE2E
uv sync --all-extras   # installs deps and dev deps from uv.lock
uv run pytest tests/   # run tests

Option 3: Manual development (pip/conda)

conda create -n standard_e2e python=3.12
conda activate standard_e2e
pip install -e ".[dev]"

Plan for E2E Autonomous Driving Datasets Support

Dataset	Cameras	Lidar	HD Map	Detections	Driving Command	Preference Trajectories
Waymo End-to-end		❌	❌	❌	✅	✅
Waymo Perception		✅	✅	✅	❌	❌
Navsim		✅	✅	✅	✅	❌
WayveScenes101		¹	❌	❌	❌	❌
Argoverse 2 Sensor		✅	✅	✅	❌	❌
Argoverse 2 Lidar	❌	✅	✅	❌	❌	❌
comma2k19	²	❌	❌	❌	❌	❌
Argoverse 2 Map Change	✅	✅	✅	❌	❌	❌
KITTI		✅	❓	❓	❓	❓
KITTI-360	+ 2 x	✅	❓	❓	❓	❓
Waymo Motion Prediction	❌	✅	✅	✅	❌	❌
Argoverse 2 Motion Forecasting [?]	❌	❌	✅	✅	❌	❌

¹ WayveScenes101 ships no sensor lidar; the lidar_pc modality is populated from the per-scene COLMAP SfM point cloud, transformed into each frame's ego frame and range-clipped so it flows through the lidar adapters. It is photogrammetric (sparse, up-to-scale), not a sensor measurement. Cameras are 5 fisheye views (forward + side arc); the ego past/future trajectory is derived from the COLMAP poses. Scenes must be extracted from the distributed scene_<NNN>.zip archives before processing.

² comma2k19 is a single forward-facing 20 Hz camera (comma EON, 1164×874, pinhole) with a fused GNSS/IMU ego pose; the ego past/future trajectory is derived from the global ECEF poses and global_position additionally carries the ego speed. It ships no lidar, HD map, 3D boxes, or driving command. Segments must be extracted from the distributed Chunk_*.zip archives before processing (as with WayveScenes101); each video.hevc is then decoded frame-by-frame, forward-only. global_position is expressed in a per-segment local frame (ECEF-axis-aligned, origin at the segment start), so absolute X/Y/Z and heading are segment-relative; the ego-relative past/future trajectories are unaffected. Native rate is 20 Hz — use --frame_stride to subsample and bound output volume.

🚀 Key Features

• Unified Dataset Interface: Work with multiple datasets through a single API
• Multimodal Support: Cameras, LiDAR (point cloud + BEV histogram), HD maps (BEV raster), trajectories, detections and more
• Flexible Preprocessing: Configurable pipelines with standardization and augmentation
• Lazy modality loading: Configured adapters declare what they consume; no decoding work is done for modalities no adapter reads
• Trajectory Management: Advanced handling of time-series vehicle data
• PyTorch Integration: Ready-to-use datasets and dataloaders

📝 Quick Start & Examples

Notebooks

• intro_tutorial.ipynb - Introduction to StandardE2E framework
• containers.ipynb - Working with data containers
• multi_dataset_training_and_filtering.ipynb - Multi-dataset training and filtering
• creating_custom_adapter.ipynb - Creating custom dataset adapters

Code Examples

Run from the project root so uv run uses the project environment. If you use pip/conda instead, activate your env and use python in place of uv run python.

1. Preprocess Waymo End-to-end dataset - Convert raw dataset to standardized format (dataset_preprocessing.py)

   uv run python examples/dataset_preprocessing.py \
     --e2e_dataset_path E2E_DATASET_PATH \
     --split {training,val,test} \
     --processed_data_path PROCESSED_DATA_PATH
   

2. Train your model - End-to-end training with multimodal data (very_simple_training.py). This example illustrates iteration over the preprocessed dataset. Also, in this example for validation we use 2 DataLoaders - full validation split and filtered validation split that only contains samples with preferred trajectories.

   uv run python examples/very_simple_training.py --processed_data_path PROCESSED_DATA_PATH
   

3. Create a unified DataLoader: This example shows how to process 2 different datasets within same DataLoader. First, please do preprocessing for Waymo E2E and Waymo Perception datasets in order to utilize them in the DataLoader with the script (prepare_datasets_waymo_e2e_perception.sh).

   The script creating_unified_dataloader.py created a unified dataloader that iterates over both Waymo E2E and Waymo Perception in one epoch providing consistent data structure.

   uv run python examples/creating_unified_dataloader.py --processed_data_path PROCESSED_DATA_PATH
   

4. Add a new dataset adapter - Guide for adding support for new datasets (adding_new_dataset.md)

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📚 Citation

If you find this project useful, you can support it by giving it a ⭐, or by contributing with your PRs / issues / feature requests. Also, if you use this project, you can greatly support it by citing our paper (arXiv:2606.04271):

@misc{konev2026standarde2e,
  title={StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets},
  author={Stepan Konev},
  year={2026},
  eprint={2606.04271},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2606.04271}
}

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