Jeonghyeon3575/EgoSpeed-MultiVehicle

EgoSpeed Multi-Vehicle Dataset

EgoSpeed Multi-Vehicle is a synchronized in-vehicle dashcam and OBD-speed dataset for vision-based ego-vehicle speed estimation and cross-vehicle domain generalization. It accompanies the paper Vehicle-Invariant Ego-Speed Estimation from In-Vehicle Dashcam Videos and the official EgoSpeed-SmartROI implementation.

The release contains recordings from five vehicle models: Avante, Malibu, Sonata, Carnival, and XM3. It is distributed in two forms so users can either run the released model immediately or reproduce the complete preprocessing pipeline from the synchronized source videos.

Downloads

File	Size	Use
EgoSpeed_model_ready_48x86_20260914.tar.zst	2.041 GiB	Direct training and evaluation
EgoSpeed_original_mp4_per_frame_csv_20260914.tar.zst	22.966 GiB	Reproduce preprocessing from synchronized 1080p MP4 and per-frame speed CSV files

Download only the model-ready archive:

hf download Jeonghyeon3575/EgoSpeed-MultiVehicle \
  --repo-type dataset \
  --include "EgoSpeed_model_ready_48x86_20260914.tar.zst" \
  --local-dir .

Download the complete release:

hf download Jeonghyeon3575/EgoSpeed-MultiVehicle \
  --repo-type dataset \
  --local-dir .

Dataset Summary

Item	Value
Vehicle models	5
Physical recordings	14
Original video resolution	1920 x 1080
Original frame rate	30 FPS
Original aligned frame/label pairs	292,248
Model-ready sampling rate	10 FPS
Model-ready frames	97,421
Model input resolution	48 x 86
Temporal clip length	13 frames

The original release contains five Avante recordings, four Malibu recordings, three Carnival recordings, one Sonata recording, and one XM3 recording.

Original Synchronized Release

Extract the archive:

tar --zstd -xf EgoSpeed_original_mp4_per_frame_csv_20260914.tar.zst

It produces:

EgoSpeed_original_mp4_per_frame_csv_20260914/
  dataset/
    README.md
    manifest.csv
    validation.json
    extract_frames.py
    recordings/
      avante_1/
        avante_1.mp4
        avante_1.csv
      ...

Each recording CSV contains:

Column	Meaning
frame_index	Zero-based index of the corresponding video frame
time_sec	Frame timestamp, equal to frame_index / 30
speed_kmh	Synchronized speed in km/h
speed_mps	Synchronized speed in m/s

The finalized OBD-speed signal is sampled at each video-frame timestamp. Any video tail without a valid speed label was removed without re-encoding the video. Consequently, every released MP4 has exactly the same number of frames as its paired CSV has data rows.

The public files do not contain a shared hardware-trigger record for the camera and OBD logger. Therefore, a constant inter-device offset cannot be independently estimated again from the released file metadata; users should use the provided synchronized per-frame labels.

Model-Ready Release

Extract the archive:

tar --zstd -xf EgoSpeed_model_ready_48x86_20260914.tar.zst

It produces:

EgoSpeedDataset/
  packed/
    avante_01/
      packed_depthnorm64.pt
    ...
  smartroi_masks/
    avante_01__smartroi_mask_u8.pt
    ...
  metadata/
    holdout_splits.json
    sequence_manifest.csv
    release_metadata.json

Each packed_depthnorm64.pt is a PyTorch dictionary containing:

Key	Shape	Typical dtype	Description
frames	(N, 1, 48, 86)	float16	Grayscale frames normalized with gray * 2 - 1
flow_rate64	(N, 2, 48, 86)	float16	RAFT-Large horizontal and vertical flow rates
speeds_mps	(N,)	float32	Synchronized speed labels in m/s
frame_indices	(N,)	integer	Indices in the 10 FPS model stream
depth_rel64	(N, 1, 48, 86)	float16	Relative inverse depth retained for historical compatibility

The final model reads frames, flow_rate64, and speeds_mps from the pack. Relative depth is not a model input; it is used only in the offline SmartROI construction. Each mask file contains masks_u8 with shape (N, 48, 86) and dtype uint8.

Public model-ready sequence names use the form <vehicle_model>_<recording_number>, such as avante_01 and carnival_03. Their numbering is a fixed public alias mapping and should not be assumed to match the raw recording numbers. The official preprocessing adapter performs this mapping automatically.

Quick Start

Place the extracted model-ready dataset next to the cloned code repository:

parent_directory/
  EgoSpeed-SmartROI/
  EgoSpeedDataset/

Then run:

git clone https://github.com/JeongHyeon2/Vehicle-Invariant-Ego-Speed-Estimation.git EgoSpeed-SmartROI
cd EgoSpeed-SmartROI
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -e .
python scripts/inspect_dataset.py
python scripts/evaluate.py --dry-run
python scripts/train_lovo.py --dry-run --holdout holdout_avante

On Windows PowerShell, activate the environment with:

.\.venv\Scripts\Activate.ps1

inspect_dataset.py, evaluate.py, and train_lovo.py automatically discover a sibling directory named EgoSpeedDataset. Use --dataset-root or the EGOSPEED_DATASET_ROOT environment variable for another location.

Run a complete training fold after the dry run succeeds:

python scripts/train_lovo.py --seed 42 --holdout holdout_avante

Reproduce the Model-Ready Data

Keep all three extracted components under one parent directory:

parent_directory/
  EgoSpeed-SmartROI/
  EgoSpeedDataset/
  EgoSpeed_original_mp4_per_frame_csv_20260914/
    dataset/
      recordings/

Install a CUDA-enabled PyTorch/torchvision build and the preprocessing extras, then run the resumable builder from the code repository:

pip install -e ".[preprocessing]"
python scripts/preprocessing/build_model_ready.py --amp

The pipeline extracts the 10 FPS RGB stream, builds normalized 48 x 86 grayscale and RAFT flow tensors, computes full-resolution MiDaS DPT-Large and RAFT-Large SmartROI masks, writes EgoSpeedDataset, and validates the result. The first run requires internet access for pretrained weights and a CUDA GPU. See the detailed preprocessing documentation.

Evaluation Protocol

The official code provides five leave-one-vehicle-out folds. In each fold, all recordings from one vehicle model are held out for testing. The released code contains five seed-42 checkpoints and supports the three paper seeds 42, 45, and 46.

Checksums

15DF1C3A09AFE28BB7CD1EA6F22F4C598B37ABADFD4FF2DEB6521F522B506CAE  EgoSpeed_model_ready_48x86_20260914.tar.zst
96AB406560C34516672E3F2C432DA5ED381BDD85C5AAF6CA158F7668874F91CC  EgoSpeed_original_mp4_per_frame_csv_20260914.tar.zst

Intended Use and Limitations

This dataset is intended for non-commercial research on ego-speed estimation, video regression, motion representation learning, and vehicle-domain generalization. It is not intended for identity recognition, surveillance, or attempts to identify road users or vehicles.

The data were collected with a limited set of five vehicle models and camera configurations. Performance measured on this release should not be interpreted as validation for all vehicles, cameras, roads, countries, weather conditions, or safety-critical deployment. The speed labels and predictions must not be used as the sole input to real-world vehicle control.

The original release contains real-world road video. Users must comply with the dataset license and applicable privacy and data-protection requirements.

License

The dataset is released under CC BY-NC 4.0. Attribution is required and commercial use is not permitted under this license. Source code in the companion GitHub repository is separately licensed under GPL-3.0.

Citation

@article{kim2026egospeed,
  title={Vehicle-Invariant Ego-Speed Estimation from In-Vehicle Dashcam Videos},
  author={Kim, Jeonghyeon and Kim, Youngwon and Lee, Jun Seong},
  journal={IEEE Access},
  year={2026}
}

The citation entry will be updated with volume, issue, pages, and DOI after publication.

Questions and Issues

For questions about the data, preprocessing, or released checkpoints, open an issue in the official GitHub repository.