41 datasets found

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

SemanticKITTI and SemanticPOSS are large-scale outdoor scene LiDAR datasets, which provide the complete point-wise labels for all 22 sequences in KITTI Odometry Benchmark.

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

The Argoverse and TrajNet++ datasets are used for multi-agent trajectory forecasting tasks.

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

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.

The BDD100k dataset is a large-scale driving dataset containing 100,000 images of various weather conditions, including clear, rainy, foggy, and night-time.

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

The NEAT dataset, used for training and evaluation of the Neural Attention Fields (NEAT) model.

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

The Kaggle SAP dataset is a publicly available dataset used for steering angle prediction in autonomous vehicles.

The SemanticKITTI dataset is a LiDAR-based semantic segmentation dataset, which consists of 10 sequences for training and 1 for validation.

The authors created a custom dataset for their experiment, consisting of 33,000 images of 320 possible object-image combinations, with 10 possible shapes, 8 possible colors, 2...

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