Description: ApolloScape, a part of Apollo, additionally offers training data for semantic segmentation (pixel-level classification of video frames, usually input for training neural networks). As of April 2021, the dataset contained 100k video frames, 80k LiDAR point clouds and trajectories covering 1000 km in urban traffic. ApolloScape also includes a scene parsing dataset covering almost 150k frames with corresponding pixel-level annotations and pose information, depth maps for static background.
When: 2019
Where: China
How:
What:
Data Categories: 3D Lidar point clouds, video, trajectories, GNSS
Data Format: Bin and txt
Lables: Different types of high qualiaty labeles available.
Size:
Hours:
Link: https://apolloscape.auto/tracking.html
License: For academic purposes only. User license at https://apolloscape.auto/license.html
Description: Intended to support odometry, metric localization, and 3D object detection for lidar, radar, and vision over a long period and multiple weather conditions
When: 2021
Where: Toronto, Canada
How: 1x Cam, 1x Lidar, 1xGNSS, 1xIMU, 1xRadar
What:
Data Categories: Lidar, radar, video, GNSS and meta data
Data Format: Point cloud data: binary, camera: png images, pose files: csv, metadata: txt and yaml
Lables: N/A
Size:
Hours:
Link: https://www.boreas.utias.utoronto.ca
License: CC Attribution
Doi:
Accessed: 2024-01-22
Boreas
AD
2021
Lidar, radar, video, GNSS and meta data
Point cloud data: binary, camera: png images, pose files: csv, metadata: txt and yaml
Description: MIT DriveSeg (Manual) Dataset is a forward facing frame-by-frame pixel level semantic labeled dataset captured from a moving vehicle during continuous daylight driving through a crowded city street. The datset is densely annotated for every pixel and every one of 5,000 video frames. The purpose of this dataset is to allow for exploration of the value of temporal dynamics information for full scene segmentation in dynamic, real-world operating environments.
The dataset is produced from the MIT-AVT dataset.
Description: The MIT-AVT Clustered Driving Scene Dataset is extracted has been generated from the MIT-AVS naturalistic driving study (https://ieeexplore.ieee.org/document/8751968).
The data has been labelled by both manual and automatic annotations.
1,156,592 10-second clips including 450 clusters of common scenes and 5601 edge cases.
When: 2021
Where: US
How: 23 Tesla Model S and Model X vehicles, 2 Volvo S90 vehicles, 2 Range Rover Evoque, and 2 Cadillac CT6 vehicles for both long-term (over a year per driver) and medium-term (one month per driver) naturalistic driving data collection. The recorded data streams include IMU, GPS, and CAN messages, and high-definition video streams of the driver's face, the driver cabin, the forward roadway, and the instrument cluster (on select vehicles). In 2019, 122 participants, 15610 days of participation, 511638 mi, and 7.1 billion video frames. The collected data cover all year, different weather conditions, and different time of day.
Description: Large-scale planning benchmark for autonomous driving. NuScene dataset continuation, with 1500 hours of driving scenes and a toolkit for measuring performance of planning techniques. Full release in Q1 2022+C9, Second sub-release available.
When: 2021
Where: Boston, Las Vegas, Pittsburgh, USA, and Singapore
How: 5x LIDAR, 8x camera, IMU, GPS
What:
Data Categories: Lidar, video, GPS, map data and meta data
Description: Synthetic Dataset generated in CARLA with synchronized data of multiple vehicles and RSUs.
When: 2022
Where: CARLA (CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4.
How: V2XSet is collected using OpenCDA, which is the first open co-simulation-based research/engineering framework integrated with prototype cooperative driving automation pipelines as well as regular automated driving components (e.g., perception, localization, planning, control). V2X-ViT is build upon OpenCOOD, which is the first Open Cooperative Detection framework for autonomous driving.
What:
Data Categories: Point cloud data, video data, metadata
Data Format: Point cloud data: pcd, video data: png, metadata: yaml, lables: yaml
Lables: Ground truth labels
Size: 100 GB
Hours: 52 sequences
Link: https://github.com/DerrickXuNu/v2x-vit
License: CC Attribution
Doi:
Accessed:
V2X-SIM
AD
2022
Point cloud data, video data, metadata
Point cloud data: pcd, video data: png, metadata: yaml, lables: yaml
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