170 datasets found

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 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.

Cross-LiDAR 3D detection dataset

Cross-LiDAR 3D detection dataset

A multi-modal dataset capturing self-driving scenes in various conditions, including different times of day and weather.

Multi-agent collaborative perception dataset and benchmark for autonomous driving

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

The VoxelNet dataset is a large-scale dataset for 3D object detection, consisting of 3D point clouds and corresponding annotations.

The Multi-View 3D Object Detection Network is a dataset for 3D object detection, consisting of 3D point clouds and corresponding annotations.

The KITTI benchmark suite is a large-scale dataset for 3D object detection, consisting of 7,481 training samples and 7,518 test samples.

Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone. While data augmentation is an effective technique to...

The V2XSet dataset is a public large-scale dataset for multi-agent perception, containing LiDAR data from different autonomous vehicles and roadside intelligent infrastructure.

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.

The Conditional Traffic Generation (CTG) model is a state-of-the-art model for controllable traffic simulation.