21 datasets found

The A9-Intersection dataset is a real-world dataset made of two RSUs positioned at 7 meters height in an intersection in Garching, Germany. Each RSU has an Ouster OS1-64 LiDAR...

The V2X-Sim 2.0 dataset is a synthetic dataset made with CARLA, containing 100 sequences, each of which has 100 samples containing point clouds obtained by 1 RSU positioned at...

The comma2k19 dataset is used to evaluate the robustness of lane detection models under physical-world adversarial attacks in autonomous driving.

Moving Object Detection (MOD) dataset for fisheye cameras, focusing on autonomous driving scenes.

The ApolloScape dataset is a large-scale dataset for autonomous driving, containing images and annotations.

nuImages is a public autonomous driving dataset, significantly different from the former two datasets in terms of resolution, context, RGB distribution and annotated classes.

The rounD dataset contains trajectories from various locations in Germany captured at a frequency of 25 Hz.

A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems.

The proposed uncertainty-aware model-based reinforcement learning framework is applied to end-to-end autonomous driving tasks.

The LAV dataset is used to evaluate the robustness of the proposed Penalty-based Imitation Learning with Cross Semantics Generation approach.

A dataset for complex activity detection in autonomous driving and surgical robotics.

Fishyscapes: A benchmark for safe semantic segmentation in autonomous driving with annotations for pedestrian and vehicle detection.

MUAD: A synthetic dataset for autonomous driving with multiple uncertainties and annotations for semantic segmentation, depth estimation, object detection, and instance...

The Stanford Drone Dataset is a well-established benchmark for human trajectory prediction in bird’s eye view. The dataset consists of 20 scenes captured using a drone in...

The dataset used in this paper is a collection of safety-critical scenarios for autonomous driving, generated using rare-event simulation techniques.

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

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.