num_samples int64 | format string | sample_shape dict | sample_stats dict |
|---|---|---|---|
850 | pickle | {
"obj_trajs": [
32,
21,
2
],
"obj_trajs_mask": [
32,
21
],
"map_polylines": [
128,
20,
2
],
"map_polylines_mask": [
128,
20
],
"center_gt_trajs": [
60,
2
],
"center_gt_trajs_mask": [
60
],
"center_gt_final_valid_idx": [],
"track_index_to_predict": []
} | {
"valid_agents": 21,
"valid_polylines": 128,
"valid_future_steps": 60
} |
YAML Metadata Warning: The task_categories "trajectory-prediction" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning: The task_categories "autonomous-driving" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Waymo Trajectory Prediction
Dataset Description
This dataset contains preprocessed trajectory prediction samples for autonomous driving research, formatted for use with DiscoBench's TrajectoryPrediction task.
- Original Dataset: Waymo Open Dataset
- Number of Samples: 850
- Format: Pickle files with numpy arrays
- Task: Multi-modal trajectory prediction
Dataset Structure
Each sample is a pickle file containing:
- obj_trajs
(32, 21, 2): Past trajectories of surrounding agents (2.1s @ 10Hz) - obj_trajs_mask
(32, 21): Validity mask for past trajectories - map_polylines
(128, 20, 2): Map polylines (lanes, boundaries) - map_polylines_mask
(128, 20): Validity mask for map polylines - center_gt_trajs
(60, 2): Ground truth future trajectory (6s @ 10Hz) - center_gt_trajs_mask
(60,): Validity mask for future trajectory - center_gt_final_valid_idx: Last valid timestep index
- track_index_to_predict: Index of the track to predict
Usage
Download Dataset
from huggingface_hub import hf_hub_download
import pickle
# Download a single sample
file_path = hf_hub_download(
repo_id="saeedrmd/trajectory-prediction-waymo",
filename="sample_0000.pkl",
repo_type="dataset"
)
# Load the sample
with open(file_path, 'rb') as f:
data = pickle.load(f)
print("Agent trajectories:", data['obj_trajs'].shape)
print("Ground truth:", data['center_gt_trajs'].shape)
Download All Samples
from huggingface_hub import snapshot_download
# Download entire dataset
dataset_path = snapshot_download(
repo_id="saeedrmd/trajectory-prediction-waymo",
repo_type="dataset"
)
print(f"Dataset downloaded to: {dataset_path}")
Use with DiscoBench
- Download the dataset using the code above
- Place the samples in your DiscoBench cache directory:
cp {dataset_path}/*.pkl /path/to/DiscoBench/cache/Waymo Trajectory Prediction/ - Update your task configuration to point to this cache directory
Dataset Statistics
- Number of samples: 850
- Average valid agents per sample: ~21
- Average valid polylines per sample: ~128
- Average valid future timesteps: ~60
Coordinate System
All trajectories and map features are in the focal agent's coordinate frame:
- Origin: Focal agent's position at current timestep
- Orientation: Aligned with focal agent's heading
- Units: Meters
Citation
If you use this dataset, please cite:
@dataset{trajectory_prediction_discobench,
title={Waymo Trajectory Prediction},
author={DiscoBench Team},
year={2025},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/datasets/saeedrmd/trajectory-prediction-waymo}}
}
License
Apache 2.0
Original Dataset Citations
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