170 datasets found

The KITTI odometry benchmark dataset is used for evaluating computer vision algorithms in autonomous driving scenarios. It contains LiDAR data collected with Velodyne HDL-64E,...

A cross-season dataset for multi-weather SLAM in autonomous driving, covering seasonal and challenging perceptual conditions.

The Waymo Open Perception dataset is a large-scale dataset for autonomous driving perception.

The dataset used in this paper is a risk measurement, risk entropy, and autonomous driving risk modeling dataset.

The dataset used in the research paper is a multi-agent environment for training the car agent, allowing it to learn human behaviors through multimodal sensory data.

The dataset used in the paper is a large-scale point cloud compression framework, which can organize sparse and un-structured point clouds in a memory-efficient way.

The dataset used in the paper is a large-scale dataset of unregulated urban intersections, with a focus on lidar-based end-to-end autonomous driving. The dataset contains a...

A real-world stereo dataset collected by robot teleoperation.

A real-world stereo dataset containing both indoor and outdoor environments via robot teleoperation.

The dataset is a real-world dataset for motion prediction in autonomous driving, containing 18-dimensional feature vectors describing the current traffic situation.

The dataset is a synthetic dataset for motion prediction, and a real-world dataset for motion prediction in autonomous driving.

A new dataset for pedestrian action prediction, extending the nuScenes dataset with 3D bounding box and behavioral annotations.

The proposed approach was evaluated on two large-scale data sets collected on the streets of several cities, where it outperformed the existing state-of-the-art.

Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths,...

AmodalSynthDrive is a synthetic multi-task multi-modal amodal perception dataset for autonomous driving. It provides multi-view camera images, 3D bounding boxes, LiDAR data, and...

AADS: Augmented autonomous driving simulation using data-driven algorithms.

A novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving.

The highD dataset is a real-world high-definition video dataset of naturalistic vehicle trajectories on German highways.

The dataset is used for lane detection in highway and city scenes.

NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving