10 datasets found

The dataset used in the paper is the Lyft Motion Prediction Dataset, which contains samples of real-world driving on a complex, urban route in Palo Alto, California.

The dataset used in the paper is a collection of images and corresponding affordances (pedestrian hazard, vehicle hazard, red traffic light, and relative heading angle) for...

The KITTI odometry dataset is a collection of 22 sequences, containing point clouds, images, and GPS recordings of inner-city traffic, residential areas, highway scenes, and...

The WOMD dataset is a large-scale interactive motion forecasting dataset for autonomous driving, which includes 2 million agents and 6 motion trajectories for 8 seconds in the...

The WoodScape dataset consists of 10,000 annotated images captured from four different view angles: Front View (FV), Mirror-View Right (MVR), Mirror-View Left (MVL), and Rear...

The KITTI Object Detection Benchmark consists of 7,481 training images and 7,518 testing images, with 3D LiDAR point clouds and camera images.

The Argoverse dataset is a large-scale dataset for autonomous driving, containing 3D point clouds, semantic segmentation masks, and instance segmentation masks.

The CARLA dataset is a complex urban-like environment with multi-agent dynamics, pedestrians, intersections, cross-traffic, roundabout, and changing weather conditions.

The Waymo Open Motion Dataset is a large-scale dataset for autonomous driving, containing 104,000 20-second frames of driving scenarios marked at 10 Hz.

The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...