🇲🇬Relevant Datasets
Selected relevant datasets towards automated driving and mixed traffic research
Last updated
Selected relevant datasets towards automated driving and mixed traffic research
Last updated
Dataset
Data Description
(Type and Volume)
Relevant Tasks
and Case Studies
Data Samples Screenshot
Image & video with annotation;
100K video clips & images,1.8TB
Perception:
Semantic segmentation;
Lane detection
Involves 7 sensor stations equipped with more than 60 SOTA and multi-modal sensors, and covered a road network of approximately 3.5 kilometres, R0, R1, R2 three different data sets
Perception;
Digital Twin;
Motion Prediction;
Motion: TFRecord format with object trajectories and corresponding 3D maps for 103,354 segments;
Perception: Lidar and Camera data, labels for 2,030 segments
Motion: Motion Prediction, Interaction, Occupancy, and Flow Prediction, Sim Agents;
Perception: Segmentation, Object Detection & Tracking, Pose Estimation
170,000 scenes around automated vehicle; 1000+ hours; zarr format with python toolkit
Motion Prediction
104 hours of videos; GPS/IMU, CAN; etc.
10000 video clips; 12 classes bounding box, tracking ID, class ID;
Currently unreachable
Perception: Object Detection & Tracking
Video, point cloud, GPS, and driver behaviour (speed and wheel); 1000 km
Driving Policy Prediction
Drone-Based Vehicle Trajectory, 1140-minutes of drone videos@30 FPS recorded at 12 different locations
VR Driving Simulation;
Digital Twin;
Sensor Simulation;
Driving Behaviour Analysis;
Safety & Crash Analysis
Drone-based collection; 110500 vehicles; 147 hours; CSV; (Highway, Interaction, Roundabout)
Behaviour Extraction & Analysis; Intention / Behaviour / Motion Prediction;
Imitation Learning;
2 grayscale cameras, 2 color cameras, 4 Edmund optics lenses, 1 3D laser scanner (10 HZ); 6 hours; 50 scenes, 180 GB
Perception: Object Detection & Tracking; Semantic and Instance Segmentation; Road/Lane Detection
1000 driving scenes; 23 object classes annotated with 3D bounding boxes at 2Hz; 1.4M camera images, 390k LIDAR sweeps, 1.4M RADAR sweeps, and 1.4M object bounding boxes in 40k keyframes
Perception: 3D Detection and Tracking; Prediction
1200h (Boston, Pittsburgh, Las Vegas and Singapore) + 838 (Las Vegas). 2D high definition maps. The states of all traffic lights are estimated. Python Toolkit is provided.
Motion Planning, Motion Prediction
1: 3D Tracking Dataset with 113 3D annotated scenes;
2: Sensor Dataset with 1,000 3D annotated scenarios (lidar, ring camera, and stereo sensor data), Lidar Dataset with 20,000 unlabeled scenarios
Perception: 3D Tracking;
Motion Forecasting
Image (video) with annotation;
133K images
Perception:
Semantic segmentation;
Lane detection
Video with semantic annotation;
>140K images (video frames)
Perception:
Semantic segmentation;
Object & Lane detection
Image & video with annotation;
Two image sets：7K and 5K
Perception:
Semantic segmentation;
Lane detection
Video, LiDAR, GPS;
10 videos
Perception:
Semantic segmentation;
Lane detection
LiDAR and stereo images with various position sensors targeting a highly complex urban environment;
tar.gz
SLAM; Odometry
Image (Traffic sign) with annotation
Perception: Object Detection
700+ images; 10+ minutes of high quality 30Hz footage with corresponding semantically labeled images at 1Hz and in part, 15Hz
Perception: Segmentation & Recognition
Video with annotation (bounding box, behavioral label);
347 videos, 170GB
Perception:
Object Detection;
Behaviour Analysis
Video with behavioral label, GPS, vehicle data;
35 videos
Behavior analysis
Video, LiDAR, GPS, vehicle with annotation (bounding box);
300GB
Perception:
Object detection, Object tracking:
End2End learning;
Imitation learning
Video (image) with annotation (vehicle and traffic sign);
3 (vehicle) + several (traffic sign) videos
Perception: Object Detection;
Video (image) with annotation;
5,000 (manual) + 20,100 (semi-auto) frames
Perception:
Object Detection, Semantic Segmentation;
Imitation learning
Image(video), LiDAR, with Semantic and Point cloud Segmentation, 3D bounding;
41,280 (image) +
12,499 (3D) + 390,000 (unlabeled sensor) frames
Perception:
Object Detection, Object Tracking;
End2End Learning;
Imitation Learning
25,000 high-resolution images;
124 semantic object categories;
100 instance-annotated categories;
Global reach, covering 6 continents
Perception:
Street-level Instance Segmentation
56,000 camera images; 7,000 LiDAR sweeps; 75 scenes of 50-100 frames each 10 annotation classes;
Full sensor suite: 1 LiDAR, 8 Cameras, Post-processed GPS/IMU;
Adverse weather conditions (snow)
Perception:
(3D) Object Detection, Object Tracking;
Trajectory Prediction
LiDAR point cloud data; LAS, XML, SHP
Perception:
(3D) Object Detection
1 year, 1000 km; 20 million images along with LIDAR, GPS, and INS ground truth
Perception:
Object Detection, Object Tracking;
Dense Reconstruction;
Localization
Two vehicle cooperation simultaneously in the same location, 410 km of the driving area, 20K LiDAR, 40K RGB, and 240K annotated 3D bounding boxes across 5 vehicle classes
Perception: Vehicle-to-Vehicle Cooperative Perception; (3D) Object Detection, Tracking, Prediction, Localization;
Sim2Real Transfer Learning
Basic safety messages (BSM), vehicle trajectories, and various driver-vehicle interaction data; CSV format
Interactive Behaviour Extraction & Analysis;
Safety Analysis;
Driving Anomaly Detection
Roundabout: 10479 trajectories, 365 mins; Unsignalized Intersection: 14867 trajectories, 433 mins; Lane change: 10933 trajectories,
133 mins; Signalized intersection: 3775 trajectories, 60 mins; High definition maps in lanelet2 format
Intention/Behaviour/Motion Prediction;
Imitation Learning;
Reinforcement Learning;
Interactive Behaviour Extraction & Analysis
