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Lane Marker Dataset
2D Polyline
Autonomous Driving
|...
许可协议: Custom

Overview

Automatically annotated lane markers using Lidar maps.

  • Over 100,000 annotated images
  • Annotations of over 100 meters
  • Resolution of 1276 x 717 pixels

A SEGMENTATION CHALLENGE

Lane markers are tricky to annotate because of their median width of only 12 cm. At farther distances, the number of pixels gets very sparse and the markers start to blend with the asphalt in the camera image.

LANE APPROXIMATIONS

While pixel-level segmentation can be very useful for localization, some automated driving systems benefit from higher level representations such as splines, clothoids, or polynomials. This section of the dataset allows for evaluating existing and novel techniques.

Automated Annotations

The Unsupervised Llamas dataset was annotated by creating high definition maps for automated driving including lane markers based on Lidar. The automated vehicle can be localized against these maps and the lane markers are projected into the camera frame. The 3D projection is optimized by minimizing the difference between already detected markers in the image and projected ones. Further improvements can likely be achieved by using better detectors, optimizing difference metrics, and adding some temporal consistency.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{llamas2019,   title={Unsupervised Labeled Lane Marker Dataset Generation Using Maps},
  author={Behrendt, Karsten and Soussan, Ryan},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2019}
}

License

https://unsupervised-llamas.com/static/boxy/non_commercial_license.pdf

数据概要
数据格式
image,
数据量
100K
文件大小
141.78GB
发布方
BOSCH
Bosch is a German multinational engineering and technology company headquartered in Gerlingen, near Stuttgart, Germany. The company was founded by Robert Bosch in Stuttgart in 1886.
| 数据量 100K | 大小 141.78GB
Lane Marker Dataset
2D Polyline
Autonomous Driving
许可协议: Custom

Overview

Automatically annotated lane markers using Lidar maps.

  • Over 100,000 annotated images
  • Annotations of over 100 meters
  • Resolution of 1276 x 717 pixels

A SEGMENTATION CHALLENGE

Lane markers are tricky to annotate because of their median width of only 12 cm. At farther distances, the number of pixels gets very sparse and the markers start to blend with the asphalt in the camera image.

LANE APPROXIMATIONS

While pixel-level segmentation can be very useful for localization, some automated driving systems benefit from higher level representations such as splines, clothoids, or polynomials. This section of the dataset allows for evaluating existing and novel techniques.

Automated Annotations

The Unsupervised Llamas dataset was annotated by creating high definition maps for automated driving including lane markers based on Lidar. The automated vehicle can be localized against these maps and the lane markers are projected into the camera frame. The 3D projection is optimized by minimizing the difference between already detected markers in the image and projected ones. Further improvements can likely be achieved by using better detectors, optimizing difference metrics, and adding some temporal consistency.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{llamas2019,   title={Unsupervised Labeled Lane Marker Dataset Generation Using Maps},
  author={Behrendt, Karsten and Soussan, Ryan},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2019}
}

License

https://unsupervised-llamas.com/static/boxy/non_commercial_license.pdf

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