48 datasets found

The dataset used for road width estimation and segmentation from UAV images, surface profile, and road surface measurement from autonomous car perspective.

The dataset used in this paper is a collection of data for path planning of autonomous vehicles under mission uncertainty using model predictive control (MPC).

Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be...

A synthetic dataset generated from BDD100K datasets using RainSD module and TSIT.

The dataset used in the experiments is the vehicle’s driving environment.

A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traf-fic participants.

A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traf-fic participants.

The NuPlan dataset is a closed-loop motion planning benchmark for autonomous vehicles.

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.

The dataset used in this paper is a collection of miles driven data from autonomous vehicles.

The dataset used in this paper is a collection of disengagement events data from autonomous vehicles.

A real-world dataset for LiDAR-based end-to-end navigation, consisting of 32km of driving data with 29km for training and 3km for testing.

The DeFIX dataset is generated by driving a rule-based CARLA autopilot to train an Imitation Learning agent.

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 Argoverse dataset is a large dataset of real-world outdoor driving scenes.

The dataset used in the paper is a set of demonstrations for an autonomous driving task, where the robot needs to maneuver through slower traffic.

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

Established an object-centric multi-view LiDAR dataset, dubbed NeRF-MVL, with carefully calibrated sensor poses and gathering multi-LiDAR sensor data from real autonomous vehicles.