--- license: cc-by-4.0 language: - en pretty_name: Car Driving Multimodal Dataset v1 task_categories: - video-classification - keypoint-detection tags: - egocentric-vision - robotics - autonomous-driving - hand-pose - human-object-interaction - embodied-ai dataset_info: features: - name: frame dtype: int64 - name: timestamp dtype: float64 splits: - name: train num_examples: 100 configs: - config_name: default data_files: - split: train path: Car_Driving/combined_dataset.csv --- # Car Driving Multimodal Dataset v1 ## Overview The **Car Driving Multimodal Dataset v1** is a publication-quality egocentric dataset designed to advance research in human-object interactions (HOI), egocentric vision, and autonomous cabin activities. It captures a driver's hand interactions, hand poses, activity recognition labels, and synthetic inertial/depth annotations from a first-person perspective. This dataset was generated using the **Hand Egocentric Multimodal Annotation Pipeline**, which integrates: - Egocentric RGB-D sequence recording. - Automated 2D and 3D hand keypoints estimation. - Hand-object interaction mapping. - Action and semantic natural language labeling. - Synthetic/pseudo sensor generation (IMU signals, motion statistics, and estimated depth). By mapping fine-grained hand pose variations alongside physical motion profiles and environment objects, this dataset serves as a benchmark for training Embodied AI and action-recognition systems in vehicle environments. --- ## Dataset Highlights - **Egocentric RGB & Monocular Depth:** High-resolution first-person recordings paired with frame-aligned monocular depth estimation. - **2D/3D Hand Pose Keypoints:** 21 landmark locations tracked in pixel coordinates and metric 3D space. - **Hand-Object Interactions (HOI):** Direct mapping of active hand-object intersections per frame. - **Object Annotations:** Detailed bounding boxes and labels for cabin objects (e.g., person, car). - **Motion Statistics & Pseudo IMU:** Extracted motion metrics and synthetic linear acceleration / angular velocity signals. - **Natural Language Semantic Annotations:** Continuous descriptive actions describing step-by-step driver events. - **Structured Export formats:** Readily importable HDF5 structure and flattened CSV format for traditional ML models. --- ## Dataset Structure The dataset contains the main metadata, annotations, videos, and raw frame exports organized under a single structure. ### Folder Layout ```text car-driving-multimodal-v1/ │ ├── README.md ├── LICENSE ├── .gitattributes │ └── Car_Driving/ ├── rgb.mp4 # Original egocentric RGB video ├── depth.mp4 # Estimated monocular depth video ├── visualization_skeleton.mp4 # Video visualizing the estimated hand skeletons ├── metadata.json # Overall video and frame processing metadata ├── manifest.json # Full execution manifest with timestamp indices ├── hand_keypoints_2d.json # Frame-wise pixel coordinates for 2D hand joints ├── hand_keypoints_3d.json # Frame-wise metric 3D hand joint locations ├── hand_object_interactions.json # Hand-to-object contact annotations per frame ├── object_annotations.json # Bounding boxes and labels of detected objects ├── actions.json # Ground-truth action label categories per frame ├── semantic_actions.json # Frame-aligned natural language action descriptions ├── motion_statistics.json # Dynamic metrics (velocity, acceleration, jerk) ├── pseudo_imu.json # Synthesized 6-DOF IMU (accelerometer, gyroscope) ├── trajectories.json # Wrist and finger-tip motion trajectories ├── task_summary.json # Execution metadata and summary statistics ├── depth_metadata.json # Technical properties of the depth camera/estimation ├── combined_dataset.csv # Flattened tabular representations of all modalities ├── dataset.h5 # Structured hierarchical HDF5 binary package ├── summary_report.txt # Plain-text execution report ├── task_summary.txt # Plain-text task summary └── rgb_frames/ # Directory containing 100 extracted JPEG frames (000001.jpg - 000100.jpg) ``` ### Annotation Files | File | Description | |------|-------------| | `metadata.json` | General video configuration and capture characteristics | | `hand_keypoints_2d.json` | Frame-wise 2D coordinates for 21 hand landmarks | | `hand_keypoints_3d.json` | Frame-wise 3D spatial points relative to the wrist | | `hand_object_interactions.json` | Contact events mapping hands to active objects | | `object_annotations.json` | Extracted object categories and bounding boxes | | `actions.json` | Action activity categorization tags | | `semantic_actions.json` | Continuous natural language activity descriptions | | `motion_statistics.json` | Computed velocity, acceleration, and jerk profiles | | `pseudo_imu.json` | Synthetic linear acceleration and angular rates | | `trajectories.json` | Spatial paths of major joints across frames | | `combined_dataset.csv` | Unified tabular annotations mapping all metrics per frame | | `dataset.h5` | Hierarchical binary format storing the entire multimodal dataset | | `rgb.mp4` | Egocentric driving camera feed | | `depth.mp4` | Visual monocular depth estimation | | `visualization_skeleton.mp4` | Overlay of 3D skeletal tracks onto the original RGB video | --- ## Dataset Statistics The following statistics describe the version 1 release: - **Total Frames:** 100 frames - **Video Duration:** 30.07 seconds - **FPS:** 29.97 FPS - **Resolution:** 1024 x 576 pixels - **Detected Object Occurrences:** 194 detections (Unique labels: `person`, `car`) - **Hand-Object Interactions:** 14 active interactions - **Annotated Actions/Activities:** 100 steps (Unique categories: `pick`, `type_or_write`, `reach`, `moving`, `idle`) --- ## Usage Below are examples of how to load and parse this dataset in Python. ### 1. Reading `metadata.json` ```python import json with open("Car_Driving/metadata.json", "r") as f: metadata = json.load(f) print(f"Video Resolution: {metadata['video_info']['width']}x{metadata['video_info']['height']}") print(f"FPS: {metadata['video_info']['fps']:.2f}") ``` ### 2. Loading `dataset.h5` ```python import h5py with h5py.File("Car_Driving/dataset.h5", "r") as hf: # Print the available datasets in the H5 hierarchy print("Keys in H5:", list(hf.keys())) # Extract 3D hand keypoints and actions keypoints_3d = hf["hand_keypoints_3d"][:] actions = hf["actions"][:] print(f"Loaded 3D Keypoints shape: {keypoints_3d.shape}") ``` ### 3. Loading `combined_dataset.csv` ```python import pandas as pd df = pd.read_csv("Car_Driving/combined_dataset.csv") print(df.head()) ``` ### 4. Parsing `hand_keypoints_3d.json` ```python import json with open("Car_Driving/hand_keypoints_3d.json", "r") as f: keypoints_data = json.load(f) # Print keypoints for the first frame first_frame_keypoints = keypoints_data[0]["keypoints"] print("First frame wrist position:", first_frame_keypoints[0]) # Wrist landmark (index 0) ``` --- ## Applications - **Egocentric Cabin Vision:** Recognizing steering, infotainment adjustments, and phone interactions. - **Robotic Control & Imitation:** Modeling human dexterity and hand postures for robotic manipulation. - **Hand Pose Estimation Benchmarking:** 2D-to-3D pose mapping from monocular ego-views. - **Action Recognition:** Fine-grained activity classification and temporal action localization (TAL). - **Vision-Language-Action (VLA) Models:** Correlating natural language action labels with real physical hand dynamics. --- ## Citation If you use this dataset in your research, please cite: ```bibtex @misc{cardrivingmultimodalv1_2026, title={Car Driving Multimodal Dataset v1}, author={DeepAnnotate AI}, year={2026}, howpublished={\url{https://huggingface.co/datasets/deepannotateai/car-driving-multimodal-v1}}, note={Version 1.0} } ``` --- ## License This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. ## Version History - **v1.0 (July 2026):** Initial public release including video sequences, 2D/3D hand pose estimation, and synthetic sensory mappings. ## Acknowledgements This dataset was generated using the **Hand Egocentric Multimodal Annotation Pipeline** developed for this project.