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| | extra_gated_button_content: "I accept the terms of the NVIDIA Autonomous Vehicle Dataset License Agreement" |
| | license: other |
| | license_name: nvidia-av-dataset |
| | license_link: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles/blob/main/LICENSE.pdf |
| | viewer: false |
| | --- |
| | |
| | # PhysicalAI Autonomous Vehicles |
| |
|
| | ## Dataset Description |
| | The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, most geographically diverse collections of |
| | multi-sensor data empowering AV researchers to build the next generation of Physical AI based end-to-end driving systems. |
| |
|
| |  |
| |
|
| | This dataset has a total of 1700 hours of driving recorded from planned data-collection drives in 25 countries and 2500+ cities. |
| | The data captures diverse traffic, weather conditions, obstacles, and pedestrians in the environment. It consists of 306,152 clips that are each 20 seconds long. |
| | The sensor data includes multi-camera and LiDAR coverage for all clips, and radar coverage for 160,761 clips. |
| |
|
| | ### Geographic Coverage |
| | Approximately 50% of the data comes from throughout the US and the remaining 50% comes from 24 EU countries. |
| |
|
| |  |
| |  |
| |
|
| | | country | count | |
| | |:--------------|--------:| |
| | | United States | 155360 | |
| | | Germany | 43900 | |
| | | France | 10364 | |
| | | Italy | 8658 | |
| | | Sweden | 7330 | |
| | | Spain | 6459 | |
| | | Portugal | 6101 | |
| | | Greece | 5885 | |
| | | Austria | 5451 | |
| | | Finland | 5176 | |
| | | Croatia | 4961 | |
| | | Netherlands | 4932 | |
| | | Denmark | 4581 | |
| | | Slovenia | 4301 | |
| | | Estonia | 4128 | |
| | | Slovakia | 4122 | |
| | | Belgium | 3753 | |
| | | Czechia | 3662 | |
| | | Lithuania | 3392 | |
| | | Poland | 3232 | |
| | | Romania | 2719 | |
| | | Luxembourg | 2620 | |
| | | Latvia | 2173 | |
| | | Hungary | 1960 | |
| | | Bulgaria | 932 | |
| |
|
| | ### Environmental and Traffic Diversity |
| | - Traffic density patterns: no traffic, light traffic, medium traffic, and heavy traffic |
| | - Road types: highways, urban, residential, and rural roads |
| | - Weather: clear, rain, snow, fog |
| | - Surface conditions: dry, wet, snow/ice |
| | - Time-of-day: daytime, nighttime |
| | - Infrastructure elements such as tunnels, bridges, roundabouts, railway crossings, toll booths, inclines, and more |
| |
|
| | ## Dataset Owner(s) |
| | NVIDIA Corporation |
| |
|
| | ## Dataset Creation Date |
| | 10/28/2025 |
| |
|
| | ## License/Terms of Use |
| | [NVIDIA Autonomous Vehicle Dataset License Agreement](https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles/blob/main/LICENSE.pdf) |
| |
|
| | ## Intended Usage |
| | This dataset can be used for **autonomous vehicle related use cases only** which can be both **commercial or non-commercial** |
| | as long as the mentioned license terms are abided by. The size and diversity of this multi-sensor dataset makes it |
| | great for research on end-to-end driving, neural reconstruction, synthetic data generation, scenario mining, and many |
| | other autonomous vehicle applications. |
| |
|
| | ## Dataset Characterization |
| | - Data Collection Method<br> |
| | - Automatic/Sensor <br> |
| |
|
| | - Labeling Method<br> |
| | - Automatic/Sensor <br> |
| |
|
| | ## Data Format |
| | We store the data separately for each sensor (camera, LiDAR and radar). Besides these sensors we also provide ego motion, |
| | calibration data, autogenerated (non-GT) machine labels, and other metadata. |
| |
|
| | Because of the significant size of this dataset, we provide all features (sensor data and autolabels) in chunks of up to 100 clips each. |
| | The exception to this chunking is clip-level metadata which we intend for researchers to use to identify which subset of chunks they are |
| | interested in downloading according to their target application. Significant storage space and bandwidth savings may be achieved by |
| | downloading only chunks corresponding to a subset of sensors, country of collection, dataset split, etc. |
| |
|
| | A python developer kit to support |
| | such workflows and additional data format documentation is available at https://github.com/NVlabs/physical_ai_av. |
| |
|
| | ### Camera Data |
| | This sensor captures visual RGB data (i.e., videos) from multiple viewpoints around the vehicle. In our dataset the following seven cameras are included: |
| | - Cross left 120 fov |
| | - Cross right 120 fov |
| | - Front wide 120 fov |
| | - Front tele 30 fov |
| | - Rear left 70 fov |
| | - Rear right 70 fov |
| | - Rear tele 30 fov |
| |
|
| | Directory structure |
| | ``` |
| | camera/ |
| | ├─ camera_front_wide_120fov/ |
| | │ ├─ camera_front_wide_120fov.chunk_0000.zip |
| | │ └─ ... |
| | └─ camera_cross_left_120fov/ |
| | └─ ... |
| | ``` |
| |
|
| | Each `chunk_xxxx.zip` contains approximately 100 1080p mp4 files recorded at 30fps. Each mp4 will be named `<clip_uuid>.camera_<field_of_view>.mp4`. |
| | Users can use this [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier) to map across different corresponding views and sensors |
| | (provided there is coverage) under the designated sensor directories. The chunks also contain frame timestamps parquets corresponding to the |
| | camera mp4 files with a UUID tag in the name. |
| |
|
| | ### LiDAR Data |
| | This directory contains 3D point cloud data recorded using a top 360 degree rotating LiDAR. |
| | ``` |
| | ├─ lidar/ |
| | └─ lidar_top_360fov/ |
| | ├─ lidar_top_360fov_clip_0000.zip |
| | ├─ ... |
| | └─ lidar_top_360fov_clip_XXXX.zip |
| | ``` |
| | Inside `lidar_top_360fov_clip_0000.zip`, there are approximately 100 lidar parquet files. Each parquet will be named |
| | `<clip_uuid>.lidar_top360_fov.parquet` and contains approximately 200 lidar spins (i.e. 10Hz capture rate for a 20sec clip). |
| |
|
| | **Parquet Schema** |
| | ``` |
| | { |
| | 'spin_index': int64, # Spin number (0, 1, 2, ...199) |
| | 'reference_timestamp': int64, # Reference timestamp (microseconds) |
| | 'draco_encoded_pointcloud': binary, # Draco-encoded point cloud |
| | } |
| | ``` |
| | The point cloud can be decoded, e.g., by using the [DracoPy](https://pypi.org/project/DracoPy/) library. |
| |
|
| | ### Radar Data |
| | This folder contains 3D radar point clouds data recorded using (up to) 10 radars located in the front bumper center, |
| | front left corner, front right corner, left side, right side, rear left corner, rear right corner, rear left, and rear right. |
| | ``` |
| | radar/ |
| | ├─ radar_corner_front_left_srr_0/ |
| | │ ├─ radar_corner_front_left_srr_0.chunk_0000.zip |
| | │ ├─ ... |
| | │ └─ radar_corner_front_left_srr_0.chunk_xxxx.zip |
| | ├─ radar_corner_front_right_srr_0/ |
| | └─ ... |
| | ``` |
| | Inside `chunk_XXXX.zip`, there are approximately 100 radar parquet files. Each parquet will be named |
| | `<clip_uuid>.radar_<field_of_view>_<configuration>.parquet`. The letters `srr` stand for short range radar, |
| | `mrr` for medium range radar, and `lrr` for long range radar. |
| |
|
| | Unlike other sensors, for a clip with radar data coverage, the radar sensors for each field of view can have varying model types, |
| | depending on the clip. Therefore, the zip files accompany the numerical reference like in `srr_0`, `srr_3` at the end to denote the radar model reference. |
| |
|
| | **Parquet Schema** |
| | ``` |
| | { |
| | # Index |
| | 'scan_index': int64, # Sequential scan number |
| | |
| | # Timestamps |
| | 'timestamp': int64, # System timestamp in microseconds |
| | 'sensor_timestamp': int64, # Sensor timestamp in microseconds |
| | |
| | # Scan Information |
| | 'num_returns': int64, # Number of detections in scan |
| | 'doppler_ambiguity': float32, # Doppler ambiguity value |
| | 'max_returns': float64, # Maximum # of returns (NaN if inapplicable) |
| | 'detection_index': int64, # Detection index within scan |
| | 'radar_model': uint8, # Radar model identifier |
| | |
| | # Detection Spatial Data |
| | 'azimuth': float32, # Horizontal angle in radians |
| | 'elevation': float32, # Vertical angle in radians |
| | 'distance': float32, # Distance to target in meters |
| | |
| | # Detection Kinematics |
| | 'radial_velocity': float32, # Radial velocity in m/s |
| | |
| | # Detection Quality |
| | 'rcs': float32, # Radar cross-section in dBsm |
| | 'snr': float32, # Signal-to-noise ratio in dB |
| | 'exist_probb': uint8, # Existence probability |
| | } |
| | ``` |
| |
|
| | ### Calibration Data |
| |
|
| | **Camera intrinsics:** Parquet files which contain parameters including [f-theta camera model](https://cdck-file-uploads-global.s3.dualstack.us-west-2.amazonaws.com/nvidia/original/3X/5/0/5043fdcfd10bd984224ac6b4d0d9b6563c685f01.pdf) polynomial coefficients. |
| | ``` |
| | { |
| | # Index (multi-level) |
| | 'clip_id': str, # Unique clip identifier UUID |
| | 'camera_name': str, # Camera sensor name (7 types) |
| | |
| | # Image Dimensions |
| | 'width': int64, # Image width in pixels |
| | 'height': int64, # Image height in pixels |
| | |
| | # Principal Point (optical center) |
| | 'cx': float64, # Principal point X coordinate (pixels) |
| | 'cy': float64, # Principal point Y coordinate (pixels) |
| | |
| | # Backward (Undistortion) f-theta Polynomial Coefficients |
| | 'bw_poly_0': float64, # Distortion polynomial coefficient 0 |
| | 'bw_poly_1': float64, # Distortion polynomial coefficient 1 |
| | 'bw_poly_2': float64, # Distortion polynomial coefficient 2 |
| | 'bw_poly_3': float64, # Distortion polynomial coefficient 3 |
| | 'bw_poly_4': float64, # Distortion polynomial coefficient 4 |
| | |
| | # Forward (Distortion) f-theta Polynomial Coefficients |
| | 'fw_poly_0': float64, # Distortion polynomial coefficient 0 |
| | 'fw_poly_1': float64, # Distortion polynomial coefficient 1 (focal length) |
| | 'fw_poly_2': float64, # Distortion polynomial coefficient 2 |
| | 'fw_poly_3': float64, # Distortion polynomial coefficient 3 |
| | 'fw_poly_4': float64, # Distortion polynomial coefficient 4 |
| | } |
| | ``` |
| |
|
| | **Sensor extrinsics:** sensor pose, i.e., quaternion rotation and x,y,z position, for 7 cameras, 1 LiDAR, and (up to) 10 radars. |
| | ``` |
| | { |
| | 'qx': float64 # Quarternions |
| | 'qy': float64 |
| | 'qz': float64 |
| | 'qw': float64 |
| | 'x' : float64 # x,y,z positions for rig coordinate frame |
| | 'y' : float64 |
| | 'z' : float64 |
| | } |
| | |
| | #Rig coordinate origin: Center of the rear axle, projected onto the ground plane. |
| | #X-axis: Points forward |
| | #Y-axis: Points left (when looking forward) |
| | #Z-axis: Points up |
| | ``` |
| |
|
| | **Vehicle dimensions** for respective clips in each chunk. |
| | ``` |
| | { |
| | # Index |
| | 'clip_id': str, # Unique clip identifier UUID |
| | |
| | # Vehicle Dimensions (all in meters) |
| | 'length': float64, # Vehicle length (front to back) |
| | 'width': float64, # Vehicle width (left to right) |
| | 'height': float64, # Vehicle height (bottom to top) |
| | 'rear_axle_to_bbox_center': float64, # Distance from rear axle to geometric center |
| | 'wheelbase': float64, # Distance between front/rear axles |
| | 'track_width': float64, # Wheel track width (left to right) |
| | } |
| | ``` |
| |
|
| | ### Labels |
| |
|
| | **Ego Motion:** in a local coordinate frame consistent across all timestamps with the origin |
| | located at the ego vehicle's position at timestamp 0, oriented such that there is 0 yaw at |
| | timestamp 0 but otherwise attitude (pitch and roll) are estimated with respect to gravity. |
| | ``` |
| | { |
| | # Timing |
| | 'timestamp': int64, # Absolute timestamp in microseconds |
| | |
| | # Pose - Orientation (Quaternion) |
| | 'qx': float64, # Quaternion X component for orientation |
| | 'qy': float64, # Quaternion Y component for orientation |
| | 'qz': float64, # Quaternion Z component for orientation |
| | 'qw': float64, # Quaternion W (scalar) for orientation |
| | |
| | # Pose - Position in World Frame (meters) |
| | 'x': float64, # X position |
| | 'y': float64, # Y position |
| | 'z': float64, # Z position |
| | |
| | # Velocity in World Frame (m/s) |
| | 'vx': float64, # X velocity |
| | 'vy': float64, # Y velocity |
| | 'vz': float64, # Z velocity |
| | |
| | # Acceleration in World Frame (m/s²) |
| | 'ax': float64, # X acceleration |
| | 'ay': float64, # Y acceleration |
| | 'az': float64, # Z acceleration |
| | |
| | # Vehicle Rotation |
| | 'curvature': float64, # Path curvature (1/meters, inverse radius) |
| | } |
| | ``` |
| |
|
| | **Objects and Road Elements:** (COMING SOON) |
| |
|
| | ### Metadata |
| | **Sensor presence parquet:** captures the sensor availability per clip |
| | ``` |
| | { |
| | # Index |
| | 'clip_id': str, # Unique clip identifier UUID |
| | |
| | # Camera Sensors (all bool - True = present, False = absent) |
| | 'camera_cross_left_120fov': bool, # Left cross-traffic camera (120° FOV) |
| | 'camera_cross_right_120fov': bool, # Right cross-traffic camera (120° FOV) |
| | 'camera_front_tele_30fov': bool, # Front telephoto camera (30° FOV) |
| | 'camera_front_wide_120fov': bool, # Front wide camera (120° FOV) |
| | 'camera_rear_left_70fov': bool, # Rear left camera (70° FOV) |
| | 'camera_rear_right_70fov': bool, # Rear right camera (70° FOV) |
| | 'camera_rear_tele_30fov': bool, # Rear telephoto camera (30° FOV) |
| | |
| | # LiDAR Sensor (bool) |
| | 'lidar_top_360fov': bool, # Top-mounted 360° LiDAR |
| | |
| | # Radar Sensors - Corner (SRR = Short Range Radar) |
| | 'radar_corner_front_left_srr_0': bool, # Front left corner radar (model type 0) |
| | 'radar_corner_front_left_srr_3': bool, # Front left corner radar (model type 3) |
| | 'radar_corner_front_right_srr_0': bool, # Front right corner radar (model type 0) |
| | 'radar_corner_front_right_srr_3': bool, # Front right corner radar (model type 3) |
| | 'radar_corner_rear_left_srr_0': bool, # Rear left corner radar (model type 0) |
| | 'radar_corner_rear_left_srr_3': bool, # Rear left corner radar (model type 3) |
| | 'radar_corner_rear_right_srr_0': bool, # Rear right corner radar (model type 0) |
| | 'radar_corner_rear_right_srr_3': bool, # Rear right corner radar (model type 3) |
| | |
| | # Radar Sensors - Front Center (LRR = Long Range Radar, MRR = Medium Range Radar) |
| | 'radar_front_center_imaging_lrr_1': bool, # Front imaging LRR (model type 1) |
| | 'radar_front_center_mrr_2': bool, # Front MRR (model type 2) |
| | 'radar_front_center_srr_0': bool, # Front center SRR (model type 0) |
| | |
| | # Radar Sensors - Rear |
| | 'radar_rear_left_mrr_2': bool, # Rear left medium range (model type 2) |
| | 'radar_rear_left_srr_0': bool, # Rear left short range (model type 0) |
| | 'radar_rear_right_mrr_2': bool, # Rear right medium range (model type 2) |
| | 'radar_rear_right_srr_0': bool, # Rear right short range (model type 0) |
| | |
| | # Radar Sensors - Side |
| | 'radar_side_left_srr_0': bool, # Left side short range (model type 0) |
| | 'radar_side_left_srr_3': bool, # Left side short range (model type 3) |
| | 'radar_side_right_srr_0': bool, # Right side short range (model type 0) |
| | 'radar_side_right_srr_3': bool, # Right side short range (model type 3) |
| | |
| | # Radar Configuration Level |
| | 'radar_config': str, # Radar config ('NA', 'low', 'med', 'high') |
| | } |
| | ``` |
| | The final `"radar_config"` column summarizes the fact that there are 4 possible instantiations |
| | of row values for the radar sensors collectively, specifically |
| | - NA (no radars present) |
| | - low (all `srr_0` radars) |
| | - med (all `srr_3` radars except for side radars, all `mrr_2` and `lrr_1` radars) |
| | - high (all `srr_3` radars, all `mrr_2` and `lrr_1` radars) |
| |
|
| | **Data collection parquet:** contains fields to filter clips by, e.g. country where clip was recorded, the month of the year and time of day. |
| | ``` |
| | { |
| | # Index |
| | 'clip_id': str, # Unique clip identifier UUID |
| | |
| | # Geographic Information |
| | 'country': str, # Country where data was collected |
| | |
| | # Temporal Information |
| | 'month': int64, # Month of collection (1-12) |
| | 'hour_of_day': int64, # Hour when clip recorded (0-23) |
| | |
| | # Vehicle Platform |
| | 'platform_class': str, # Vehicle platform type (hyperion_8/8.1) |
| | } |
| | ``` |
| |
|
| | ## Dataset Quantification |
| | - Record Count: 1700 hours / 306,152 clips of driving data organized into 20s long clips |
| | - Feature Count: 7 cameras, 1 lidar, (up to) 10 radar, ego motion, calibration, machine labels |
| | - Measurement of Total Data Storage: ~100TB |
| |
|
| | ## References |
| | - [Developer kit and additional documentation on GitHub](https://github.com/NVlabs/physical_ai_av) |
| | - For data mining and curation, NVIDIA also provides tools like [Cosmos Dataset Search (CDS)](https://github.com/NVIDIA-Omniverse-blueprints/cosmos-dataset-search) |
| | for multimodal semantic search with text and video queries. A subset of this dataset will be explorable through a [CDS Preview Experience](https://build.nvidia.com/nvidia/cosmos-dataset-search). |
| |
|
| | ## Ethical Considerations |
| | NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a |
| | wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their |
| | internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. |
| |
|
| | Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
| |
|
