170 datasets found

KITTI 3D object detection dataset, containing 7481 training images and 7518 testing images.

BEVFusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation

The ONCE dataset is a large-scale dataset for autonomous driving, containing 581 sequences composed of 20 labeled frames and 561 unlabeled frames.

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...

BDD100K Dataset is a large-scale dataset for autonomous driving, containing 100,000 images, with 20,000 images for training and 80,000 images for testing.

The Waymo Open Dataset and the Waymo Open Motion Dataset are used in this paper.

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

Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving with annotations for pedestrian and vehicle detection.

MUAD: A synthetic dataset for autonomous driving with multiple uncertainties and annotations for semantic segmentation, depth estimation, object detection, and instance...

The dataset used in this paper is a real-world vehicle point cloud collected from a real vehicle self-driving process.

Argoverse2 is an open-source evolution of the original Argoverse

Openlane and Argoverse2 are public datasets used in autonomous driving

The Stanford Drone Dataset is a well-established benchmark for human trajectory prediction in bird’s eye view. The dataset consists of 20 scenes captured using a drone in...

Waymo Open Dataset (validation set)

The dataset used in this paper is a collection of safety-critical scenarios for autonomous driving, generated using rare-event simulation techniques.

A benchmark for stereo matching and depth estimation.

A public PSV dataset for parking slot and lane markings segmentation

Large dataset for stereo matching, optical flow, and scene flow estimation

The dataset used for evaluation of the proposed method. It contains images of humans and faces, with pre-annotated bounding boxes for persons and faces.

The dataset used for training the inconsistent behaviour predictor of DeepGuard.