89 datasets found

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

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 LISA Amazon-MLSL Vehicle Attributes (LAVA) dataset is a dataset of annotated traffic lights with various features, including color, directionality, and occlusion level.

The LAVA Salient Lights Dataset is a dataset of annotated traffic lights with a salience property, focusing on traffic lights that affect the driver’s future decisions.

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

A benchmark for stereo matching and depth estimation.

The LLAMAS dataset contains more than 100,000 high-resolution images (1276 × 717), collected primarily from highway scenarios.

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.

Custom traffic gesture dataset containing measurements of eight different gestures for 35 participants.

The dataset used in the paper is the NuScenes dataset, which contains LiDAR point clouds and corresponding semantic annotations.

KITTI 2012 is a real-world dataset in the outdoor scenario, and contains 194 training and 195 testing stereo image pairs with the size of 376 × 1240.

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

The proposed method extracts a complete lane graph from a single onboard camera image. It uses a transformer-based Wasserstein Autoencoder to process a lane graph and produce a...

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

The KITTI Vision Benchmark Suite is a dataset used for object detection and tracking in autonomous vehicles.

The KITTI object detection benchmark dataset.

The TrafficGen dataset is a collection of traffic scenarios generated using a generative model.

The Bi-Level Imitation Learning System (BITS) is a model that utilizes a two-tiered imitation learning strategy for traffic prediction tasks.