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Lane Level Localization on a 3D Map
2D Box
Fusion Box
Autonomous Driving
|...
许可协议: Unknown

Overview

The Lane Level Localization dataset was collected on a highway in San Francisco with the following properties:* Reasonable traffic* Multiple lane highway* Reasonable weather conditions* The road markings are in good condition* Data was collected over 20km. We will provide 10km of the data to participants to develop their algorithms, and the remainder will be used for evaluation.* Ground truth data of camera locations will be provided for the training set.* The car makes reasonably frequent lane changes during the collection.InputWe will offer a training dataset to each team to prototype and test their methodology. It contains:Images: these images are acquired with a commercial webcam mounted on top of a car and have the following properties:* 10 HZ* RGB color, 800 x 600 resolution* GPS data: a set of consumer phone grade GPS points with time stamp synchronized with the image timestamp* 3D map for the driven road segment including:* Road and lane boundaries (including the boundary type e.g., road edge, solid marking, dashed marking)* Marking color (white or yellow)* Elevated objects in voxels near the roadway* Traffic sign location and text content* Camera calibration parametersFigure Example. A visualization of training dataset: green lines represent lane marking and road boundary, yellow points represent occupancy grid corners and red polygons represent sign corners.Details are included in README of the test data to be downloaded.Contact Andi Zang for data download and submission information

数据概要
数据格式
video, point cloud, image,
数据量
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文件大小
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| 数据量 -- | 大小 --
Lane Level Localization on a 3D Map
2D Box Fusion Box
Autonomous Driving
许可协议: Unknown

Overview

The Lane Level Localization dataset was collected on a highway in San Francisco with the following properties:* Reasonable traffic* Multiple lane highway* Reasonable weather conditions* The road markings are in good condition* Data was collected over 20km. We will provide 10km of the data to participants to develop their algorithms, and the remainder will be used for evaluation.* Ground truth data of camera locations will be provided for the training set.* The car makes reasonably frequent lane changes during the collection.InputWe will offer a training dataset to each team to prototype and test their methodology. It contains:Images: these images are acquired with a commercial webcam mounted on top of a car and have the following properties:* 10 HZ* RGB color, 800 x 600 resolution* GPS data: a set of consumer phone grade GPS points with time stamp synchronized with the image timestamp* 3D map for the driven road segment including:* Road and lane boundaries (including the boundary type e.g., road edge, solid marking, dashed marking)* Marking color (white or yellow)* Elevated objects in voxels near the roadway* Traffic sign location and text content* Camera calibration parametersFigure Example. A visualization of training dataset: green lines represent lane marking and road boundary, yellow points represent occupancy grid corners and red polygons represent sign corners.Details are included in README of the test data to be downloaded.Contact Andi Zang for data download and submission information

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