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trajectory-prediction-waymo

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trajectory-prediction
autonomous-driving
argoverse2
nuscenes
discobench
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  2. metadata.json +38 -0
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ task_categories:
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+ - trajectory-prediction
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+ - autonomous-driving
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+ tags:
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+ - trajectory-prediction
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+ - autonomous-driving
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+ - argoverse2
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+ - nuscenes
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+ - discobench
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+ size_categories:
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+ - n<1K
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+ ---
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+
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+ # Waymo Trajectory Prediction
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+
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+ ## Dataset Description
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+
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+ This dataset contains preprocessed trajectory prediction samples for autonomous driving research,
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+ formatted for use with DiscoBench's TrajectoryPrediction task.
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+
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+ - **Original Dataset**: Waymo Open Dataset
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+ - **Number of Samples**: 850
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+ - **Format**: Pickle files with numpy arrays
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+ - **Task**: Multi-modal trajectory prediction
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+
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+ ## Dataset Structure
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+
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+ Each sample is a pickle file containing:
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+
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+ - **obj_trajs** `(32, 21, 2)`: Past trajectories of surrounding agents (2.1s @ 10Hz)
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+ - **obj_trajs_mask** `(32, 21)`: Validity mask for past trajectories
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+ - **map_polylines** `(128, 20, 2)`: Map polylines (lanes, boundaries)
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+ - **map_polylines_mask** `(128, 20)`: Validity mask for map polylines
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+ - **center_gt_trajs** `(60, 2)`: Ground truth future trajectory (6s @ 10Hz)
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+ - **center_gt_trajs_mask** `(60,)`: Validity mask for future trajectory
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+ - **center_gt_final_valid_idx**: Last valid timestep index
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+ - **track_index_to_predict**: Index of the track to predict
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+
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+ ## Usage
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+
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+ ### Download Dataset
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+
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+ ```python
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+ from huggingface_hub import hf_hub_download
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+ import pickle
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+
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+ # Download a single sample
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+ file_path = hf_hub_download(
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+ repo_id="saeedrmd/trajectory-prediction-waymo",
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+ filename="sample_0000.pkl",
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+ repo_type="dataset"
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+ )
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+
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+ # Load the sample
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+ with open(file_path, 'rb') as f:
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+ data = pickle.load(f)
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+
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+ print("Agent trajectories:", data['obj_trajs'].shape)
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+ print("Ground truth:", data['center_gt_trajs'].shape)
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+ ```
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+
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+ ### Download All Samples
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+
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+ ```python
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+ from huggingface_hub import snapshot_download
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+
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+ # Download entire dataset
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+ dataset_path = snapshot_download(
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+ repo_id="saeedrmd/trajectory-prediction-waymo",
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+ repo_type="dataset"
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+ )
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+
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+ print(f"Dataset downloaded to: {dataset_path}")
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+ ```
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+
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+ ### Use with DiscoBench
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+
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+ 1. Download the dataset using the code above
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+ 2. Place the samples in your DiscoBench cache directory:
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+ ```bash
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+ cp {dataset_path}/*.pkl /path/to/DiscoBench/cache/Waymo Trajectory Prediction/
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+ ```
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+ 3. Update your task configuration to point to this cache directory
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+
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+ ## Dataset Statistics
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+
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+ - Number of samples: 850
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+ - Average valid agents per sample: ~21
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+ - Average valid polylines per sample: ~128
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+ - Average valid future timesteps: ~60
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+
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+
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+ ## Coordinate System
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+
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+ All trajectories and map features are in the focal agent's coordinate frame:
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+ - **Origin**: Focal agent's position at current timestep
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+ - **Orientation**: Aligned with focal agent's heading
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+ - **Units**: Meters
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+
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+ ## Citation
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+
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+ If you use this dataset, please cite:
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+
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+ ```bibtex
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+ @dataset{trajectory_prediction_discobench,
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+ title={Waymo Trajectory Prediction},
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+ author={DiscoBench Team},
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+ year={2025},
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+ publisher={Hugging Face},
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+ howpublished={\url{https://huggingface.co/datasets/saeedrmd/trajectory-prediction-waymo}}
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+ }
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+ ```
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+
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+ ## License
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+
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+ Apache 2.0
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+
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+ ## Original Dataset Citations
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+
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+
metadata.json ADDED
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+ {
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+ "num_samples": 850,
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+ "format": "pickle",
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+ "sample_shape": {
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+ "obj_trajs": [
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+ 32,
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+ 32,
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+ "map_polylines": [
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+ ],
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+ "map_polylines_mask": [
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+ 128,
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+ 20
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+ ],
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+ "center_gt_trajs": [
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+ 60,
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+ 2
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+ ],
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+ "center_gt_trajs_mask": [
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+ 60
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+ ],
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+ "center_gt_final_valid_idx": [],
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+ "track_index_to_predict": []
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+ },
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+ "sample_stats": {
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+ "valid_agents": 21,
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+ "valid_polylines": 128,
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+ "valid_future_steps": 60
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+ }
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+ }
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+ oid sha256:e892282238965b1e03ab724b76f124ed4c43a385c93e684495b518742522c90a
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+ size 40078
sample_000047.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5e64f4279753e916b51b911129f5da751ac06f7357d9d10dcae110265d2f6035
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+ size 40078