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nuScenes by Motional
Large-scale open source dataset for autonomous driving.
For commercial use, please visit the nuScenes website directly.
Overview
Support for computer vision and autonomous driving research
nuScenes by Motional is a public, large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving scenarios using the full suite of sensors from a real autonomous vehicle. Motional made nuScenes and the follow-on datasets publicly available to help advance industry research.
Data Collection
Careful scene planning by Motional
The nuScenes dataset features approximately 15h of real-world driving data from Boston and Singapore, including Boston's new Seaport district and Singapore’s One North, Queenstown, and Holland Village districts. Driving routes are carefully chosen to capture challenging scenarios, including locations, times, and weather conditions.
Car Setup
Vehicle, Sensor and Camera Details
Two identical cars with identical sensor layouts were used to drive in Boston and Singapore.
Refer to the image below for the placement of the sensors:
- 6
- 12Hz capture frequency
- Evetar Lens N118B05518W F1.8 f5.5mm 1/1.8"
- 1/1.8" CMOS sensor of 1600x1200 resolution
- Bayer8 format for 1 byte per pixel encoding
- 1600x900 ROI is cropped from the original resolution to reduce processing and transmission bandwidth
- Auto exposure with exposure time limited to the maximum of 20 ms
- Images are unpacked to BGR format and compressed to JPEG
Cameras - 1
- 20Hz capture frequency
- 32 beams, 1080 (+-10) points per ring
- 32 channels
- 360° Horizontal FOV, +10° to -30° Vertical FOV, uniform azimuth angles
- 80m-100m Range, Usable returns up to 70 meters, ± 2 cm accuracy
- Up to ~1.39 Million Points per Second
Spinning LiDAR - 5
- 77GHz
- 13Hz capture frequency
- Independently measures distance and velocity in one cycle using Frequency Modulated Continuous Wave
- Up to 250m distance
- Velocity accuracy of ±0.1 km/h
Long Range RADAR Sensor
Flythrough of the nuScenes Teaser
Sensor Calibration
Our multi-sensor dataset uses sensors that have been calibrated for the extrinsics and intrinsics of every sensor
LiDAR extrinsics
Camera extrinsics
Camera intrinsic calibration
IMU extrinsics
Sensor Synchronization
In order to achieve cross-modality data alignment between the LiDAR and the cameras, the exposure on each camera was triggered when the top LiDAR sweeps across the center of the camera’s FOV. This method was selected as it generally yields good data alignment. Note that the cameras run at 12Hz while the LiDAR runs at 20Hz.
The 12 camera exposures are spread as evenly as possible across the 20 LiDAR scans, so not all LiDAR scans have a corresponding camera frame.
Reducing the frame rate of the cameras to 12Hz helps to reduce the compute, bandwidth and storage requirement of the perception system.
Data Annotation
Complex Label Taxonomy
Instances Per Label
493,322
vehicle.car
208,240
human.pedestrian.adult
152,087
movable_object.barrier
208,240
movable_object.trafficcone
88,519
vehicle.truck
24,860
vehicle.trailer
14,671
vehicle.construction
14,501
vehicle.bus.rigid
Get Started with nuScenes
Ready to get started with nuScenes? This tutorial will give you an overview of the dataset without the need to download it.
