26 datasets found

KITTI tracking dataset provides 21 training and 29 test sequences. The dataset provides 2D bounding box annotations for cars, pedestrians, and 6 other classes, but only the...

The dataset used in this paper is a set of vehicle models and obstacle avoidance formulations.

TUM-MLS-2016: An annotated mobile lidar dataset of the TUM City Campus for semantic point cloud interpretation in urban areas.

Developing and testing robust autonomy: The university of sydney campus data set.

The dataset contains human driving recordings in roads and parking lots.

The Waymo dataset is a LiDAR-based semantic segmentation dataset, which consists of 798 sequences for training and 202 for validation.

The Udacity dataset has images recorded while driving on highways and residential roads (with and without lane markings) in clear weather during daytime.

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 dataset used in this paper is a human demonstration dataset for autonomous vehicles, consisting of images from a center camera.

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 Trajnet++ benchmark provides a fixed data split and unified pre-processing for these datasets.

Trajectory prediction is a crucial task for the safe operation of an autonomous vehicle. The task is known as human trajectory prediction and aims to predict the future...

Trajectory prediction plays an essential role in autonomous vehicles. While numerous strategies have been developed to enhance the robustness of trajectory prediction models,...

Multispectral object detection for autonomous vehicles

The Ford Multi AV dataset contains images of vehicles from different angles and lighting conditions.

The KITTI odometry dataset is a benchmark for evaluating visual odometry and other computer vision tasks. It contains a large collection of images and corresponding ground-truth...

The SynthCity dataset is used to generate synthetic LiDAR point cloud data.

The GTA-V dataset is used to generate synthetic LiDAR point cloud data.

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 dataset used in the paper is a synthetic LiDAR point cloud dataset generated using the CARLA simulator.